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Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 142 (from laksa162)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa142.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.amazon.science/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.amazon.science/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.amazon.science\/","crawl_time":1776169102,"first_indexed_time":1578307021,"http_code":200,"src_unparsed":"science,amazon!www,\/ s443","src_root_hash":"3942493779428927542","history_drop_reason":null,"meta_title":"Amazon Science homepage","meta_descriptions":["Learn about Amazon's scientific research, science community, and career opportunities in artificial intelligence (AI), machine learning (ML), computer vision, robotics, quantum, economics and more."],"attrs_boilerpipe_text":"Data Scientist II, Amazon Business\nUS, WA, Seattle\nCome be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation\nComm Systems Engineer, Wireless Systems\nUS, TX, Austin\nAmazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.\nApplied Scientist II, Industrial Robotics Group\nUS, MA, N.reading\nAmazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Work on design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation\nSenior Applied Scientist, Fauna\nUS, NY, New York\nWe are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers\nSenior Applied Scientist, Applied AI\nUS, WA, Bellevue\nWe are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals.\nSenior Applied Scientist , Buyer Risk Prevention (BRP)\nIN, KA, Bengaluru\nDo you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI\/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions\nSr. Applied Scientist, Sponsored Products Off-Search Sourcing\nUS, CA, Palo Alto\nThe Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and\/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and\/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and\/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond!\nApplied Scientist, Artificial General Intelligence, AGI Data Services\nUS, MA, Boston\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.\nApplied Scientist, Artificial General Intelligence, AGI Data Services\nUS, MA, Boston\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.\nApplied Scientist, Artificial General Intelligence, AGI Data Services\nUS, MA, Boston\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.","attrs_markdown":"- [Research](https:\/\/www.amazon.science\/research-areas)\n  \n  - Research\n  - Research areas\n    \n    - [![Automated reasoning.svg](https:\/\/cdn.amazon.science\/7c\/fb\/3a915dcf44b3ae2cac79acf280d8\/automated-reasoning.svg)Automated reasoning](https:\/\/www.amazon.science\/research-areas\/automated-reasoning)\n    - [![Reduced padding - Research areas](https:\/\/cdn.amazon.science\/26\/0a\/47bb5de4438483f43551f13963d9\/cloud-and-systems.svg)Cloud and systems](https:\/\/www.amazon.science\/research-areas\/cloud-and-systems)\n    - [![Reduced padding - Research areas](https:\/\/cdn.amazon.science\/6a\/16\/a6e53bfe46ceab77d292a8cea6af\/computer-vision.svg)Computer vision](https:\/\/www.amazon.science\/research-areas\/computer-vision)\n    - 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[Research stories](https:\/\/www.amazon.science\/research-awards\/success-stories)\n    - [Recipients](https:\/\/www.amazon.science\/research-awards\/recipients)\n  - Amazon Nova AI Challenge\n    \n    - [Overview](https:\/\/www.amazon.science\/nova-ai-challenge)\n    - [Rules](https:\/\/www.amazon.science\/nova-ai-challenge\/rules)\n    - [FAQs](https:\/\/www.amazon.science\/nova-ai-challenge\/faqs)\n    - [Teams](https:\/\/www.amazon.science\/nova-ai-challenge\/teams\/)\n  - Research collaborations\n    \n    - [Overview](https:\/\/www.amazon.science\/academic-engagements)\n    - [Carnegie Mellon University](https:\/\/www.amazon.science\/news\/amazon-and-carnegie-mellon-university-launch-strategic-ai-innovation-hub)\n    - [Columbia University](https:\/\/www.amazon.science\/academic-engagements\/columbia-engineering-and-amazon-announce-creation-of-new-york-research-center)\n    - [Hampton University](https:\/\/www.amazon.science\/academic-engagements\/amazon-robotics-hampton-university-team-up-to-establish-robotics-program)\n    - [Howard University](https:\/\/www.amazon.science\/news-and-features\/amazon-and-howard-announce-expansion-of-academic-collaboration)\n    - [IIT Bombay](https:\/\/www.amazon.science\/news-and-features\/amazon-and-iit-bombay-launch-multiyear-collaboration)\n    - [Johns Hopkins University](https:\/\/www.amazon.science\/academic-engagements\/amazon-and-johns-hopkins-announce-new-ai-institute)\n    - [Max Planck Society](https:\/\/www.amazon.science\/academic-engagements\/amazon-and-max-planck-society-launch-science-hub)\n    - [MIT](https:\/\/www.amazon.science\/academic-engagements\/amazon-and-mit-establish-science-hub)\n    - [Tennessee State University](https:\/\/www.amazon.science\/latest-news\/amazon-and-tennessee-state-university-announce-academic-collaboration)\n    - [University of California, Los Angeles](https:\/\/www.amazon.science\/academic-engagements\/amazon-and-ucla-establish-science-hub-for-humanity-and-ai)\n    - [University of Illinois Urbana-Champaign](https:\/\/www.amazon.science\/news-and-features\/amazon-launches-the-aice-center-at-the-university-of-illinois-urbana-champaign)\n    - [University of Southern California](https:\/\/www.amazon.science\/academic-engagements\/usc-and-amazon-establish-center-for-secure-and-trusted-machine-learning)\n    - [University of Texas at Austin](https:\/\/www.amazon.science\/news-and-features\/amazon-and-university-of-texas-at-austin-launch-science-hub)\n    - [Virginia Tech](https:\/\/www.amazon.science\/academic-engagements\/amazon-and-virginia-tech-launch-ai-and-ml-research-initiative)\n    - [University of Washington](https:\/\/www.amazon.science\/academic-engagements\/new-uw-amazon-science-hub-launches)\n- Resources\n  \n  - - [Code and datasets](https:\/\/www.amazon.science\/code-and-datasets)\n    - [AGI Labs Meet the team building useful AI agents.](https:\/\/labs.amazon.science\/)\n    - [Amazon Nova Try Amazon’s frontier foundation models.](https:\/\/nova.amazon.com\/)\n  \n  - - [Code and datasets](https:\/\/www.amazon.science\/code-and-datasets)\n    - [AGI Labs Meet the team building useful AI agents.](https:\/\/labs.amazon.science\/)\n    - [Amazon Nova Try Amazon’s frontier foundation models.](https:\/\/nova.amazon.com\/)\n- [Careers](https:\/\/www.amazon.science\/careers)\n  \n  - - [Careers Explore our open roles.](https:\/\/www.amazon.science\/careers)\n    - [Amazon Scholars Faculty research opportunities on industry-scale technical challenges.](https:\/\/www.amazon.science\/scholars)\n    - [Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists.](https:\/\/www.amazon.science\/postdoctoral-science-program)\n  \n  - - [Careers Explore our open roles.](https:\/\/www.amazon.science\/careers)\n    - [Amazon Scholars Faculty research opportunities on industry-scale technical challenges.](https:\/\/www.amazon.science\/scholars)\n    - [Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists.](https:\/\/www.amazon.science\/postdoctoral-science-program)\n\n![RLGyms02-Option02-Detailed-Homepage.jpg](https:\/\/cdn.amazon.science\/dims4\/default\/b44aaff\/2147483647\/strip\/true\/crop\/1920x1080+0+0\/resize\/1440x810!\/quality\/90\/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fd9%2F9e%2Fe4e1c24b47ada232d516b5d10d70%2Frlgyms02-option02-detailed-homepage.jpg)\n\n\\`\n\n[It looks like a simple form. It's actually 40 years of software.](https:\/\/www.amazon.science\/blog\/how-agentic-ai-helps-heal-the-systems-we-cant-replace)\n\nAdding a pet to your flight sounds like a one-click task, but every click passes through layers of software dating back to the 1960s. Amazon's AGI Lab trains AI agents not to replace these brittle systems but to learn them deeply enough to finally make them work.\n\n[Read more](https:\/\/www.amazon.science\/blog\/how-agentic-ai-helps-heal-the-systems-we-cant-replace)\n\n![TextToSpeech-16x9.gif](https:\/\/cdn.amazon.science\/dims4\/default\/cf44c6a\/2147483647\/strip\/true\/crop\/1280x720+0+0\/resize\/1440x810!\/quality\/90\/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa0%2F98%2F78f89609444293f87d8acc871c47%2Ftexttospeech-16x9.gif)\n\n\\`\n\n[Improving quality and robustness in LLM-based text-to-speech systems](https:\/\/www.amazon.science\/blog\/improving-quality-and-robustness-in-llm-based-text-to-speech-systems)\n\nLow-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.\n\n[Read more](https:\/\/www.amazon.science\/blog\/improving-quality-and-robustness-in-llm-based-text-to-speech-systems)\n\n![MathResearch-01-16x9.png](https:\/\/cdn.amazon.science\/dims4\/default\/b715d5c\/2147483647\/strip\/true\/crop\/1920x1080+0+0\/resize\/1440x810!\/quality\/90\/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F37%2F23%2Fb207cbc54257bba9555bd6824c15%2Fmathresearch-01-16x9.png)\n\n\\`\n\n[How AI is changing the nature of mathematical research](https:\/\/www.amazon.science\/blog\/how-ai-is-changing-the-nature-of-mathematical-research)\n\nWhat machine learning theorists learned using AI agents to generate proofs — and what comes next.\n\n[Read more](https:\/\/www.amazon.science\/blog\/how-ai-is-changing-the-nature-of-mathematical-research)\n\n![HumanAI-01-16x9.png](https:\/\/cdn.amazon.science\/dims4\/default\/09b6850\/2147483647\/strip\/true\/crop\/1920x1080+0+0\/resize\/1440x810!\/quality\/90\/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F5f%2Ffd%2Fbf22bfe241c3ac6d001d34277eef%2Fhumanai-01-16x9.png)\n\n\\`\n\n[Designing AI agents that know when to step back](https:\/\/www.amazon.science\/blog\/designing-ai-agents-that-know-when-to-step-back)\n\nAs AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation.\n\n[Read more](https:\/\/www.amazon.science\/blog\/designing-ai-agents-that-know-when-to-step-back)\n\nIt looks like a simple form. It's actually 40 years of software.\n\nImproving quality and robustness in LLM-based text-to-speech systems\n\nHow AI is changing the nature of mathematical research\n\nDesigning AI agents that know when to step back\n\n# Customer-obsessed science\n![Amazon Science homepage.jpeg](https:\/\/cdn.amazon.science\/dims4\/default\/39c1939\/2147483647\/strip\/true\/crop\/1385x1200+208+0\/resize\/435x377!\/quality\/90\/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F48%2F0f%2F1db2f1004b82a99a0175ff391d53%2Famazon-science-homepage.jpeg)\n\n![EchoFrame\\_Animated\\_121124 (1).gif](https:\/\/cdn.amazon.science\/dims4\/default\/db48e94\/2147483647\/strip\/true\/crop\/646x563+177+0\/resize\/218x190!\/quality\/90\/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F8a%2F79%2Fad103fb544aaa2fbdf0745c366f1%2Fechoframe-animated-121124-1.gif)\n\n## Research areas\n[![AutomatedReasoning.svg]() Automated reasoning](https:\/\/www.amazon.science\/research-areas\/automated-reasoning)\n\n[![Cloud.svg]() Cloud and systems](https:\/\/www.amazon.science\/research-areas\/cloud-and-systems)\n\n[![ComputerVision.svg]() Computer vision](https:\/\/www.amazon.science\/research-areas\/computer-vision)\n\n[![ConversationalAI.svg]() Conversational AI](https:\/\/www.amazon.science\/research-areas\/conversational-ai-natural-language-processing)\n\n[![Economics.svg]() Economics](https:\/\/www.amazon.science\/research-areas\/economics)\n\n[![InfoManagement.svg]() Information and knowledge management](https:\/\/www.amazon.science\/research-areas\/information-and-knowledge-management)\n\n[![MachineLearning.svg]() Machine learning](https:\/\/www.amazon.science\/research-areas\/machine-learning)\n\n[![Operations.svg]() Operations research and optimization](https:\/\/www.amazon.science\/research-areas\/operations-research-and-optimization)\n\n[![Quantum Technologies.svg]() Quantum technologies](https:\/\/www.amazon.science\/research-areas\/quantum-technologies)\n\n[![Robotics.svg]() Robotics](https:\/\/www.amazon.science\/research-areas\/robotics)\n\n[![Search.svg]() Search and information retrieval](https:\/\/www.amazon.science\/research-areas\/search-and-information-retrieval)\n\n[![Security.svg]() Security, privacy, and abuse prevention](https:\/\/www.amazon.science\/research-areas\/security-privacy-and-abuse-prevention)\n\n[![Sustainability.svg]() Sustainability](https:\/\/www.amazon.science\/research-areas\/sustainability)\n\n## From the blog\n[View all](https:\/\/www.amazon.science\/blog)\n\nTechnical deep-dives and perspectives from our scientists.\n\n[View all](https:\/\/www.amazon.science\/blog)\n\n- [![generation2.jpg](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMzdweCIgd2lkdGg9IjYwMHB4Ij48L3N2Zz4=)](https:\/\/www.amazon.science\/blog\/how-amazon-uses-agentic-ai-for-vulnerability-detection-at-global-scale)\n  \n  [How Amazon uses agentic AI for vulnerability detection at global scale](https:\/\/www.amazon.science\/blog\/how-amazon-uses-agentic-ai-for-vulnerability-detection-at-global-scale)\n  \n  April 8, 2026\n  \n  6 min read\n  \n  Amazon’s RuleForge system uses agentic AI to generate production-ready detection rules 336% faster than traditional methods.\n  \n  [Security, privacy, and abuse prevention](https:\/\/www.amazon.science\/research-areas\/security-privacy-and-abuse-prevention)\n- [![mlkem-native v4.JPG](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https:\/\/www.amazon.science\/blog\/verifying-and-optimizing-post-quantum-cryptography-at-amazon)\n  \n  [Verifying and optimizing post-quantum cryptography at Amazon](https:\/\/www.amazon.science\/blog\/verifying-and-optimizing-post-quantum-cryptography-at-amazon)\n  \n  April 7, 2026\n  \n  13 min read\n  \n  [Automated reasoning](https:\/\/www.amazon.science\/research-areas\/automated-reasoning)\n- [![The basic XEX block encryption structure with the XOR-Encrypt-XOR flow v3.jpg](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https:\/\/www.amazon.science\/blog\/formally-verified-aes-xts-the-first-aes-algorithm-to-join-s2n-bignum)\n  \n  [Formally verified AES-XTS: The first AES algorithm to join s2n-bignum](https:\/\/www.amazon.science\/blog\/formally-verified-aes-xts-the-first-aes-algorithm-to-join-s2n-bignum)\n  \n  March 20, 2026\n  \n  15 min read\n  \n  [Automated reasoning](https:\/\/www.amazon.science\/research-areas\/automated-reasoning)\n- [![LoRA.16x9.png](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https:\/\/www.amazon.science\/blog\/optimizing-lora-target-module-selection-for-efficient-fine-tuning)\n  \n  [Optimizing LoRA target module selection for efficient fine tuning](https:\/\/www.amazon.science\/blog\/optimizing-lora-target-module-selection-for-efficient-fine-tuning)\n  \n  March 19, 2026\n  \n  11 min read\n  \n  [Machine learning](https:\/\/www.amazon.science\/research-areas\/machine-learning)\n- [![ZebraTime-16x9-01.png](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https:\/\/www.amazon.science\/blog\/intelligence-isnt-about-parameter-count-its-about-time)\n  \n  [Intelligence isn’t about parameter count. It’s about time.](https:\/\/www.amazon.science\/blog\/intelligence-isnt-about-parameter-count-its-about-time)\n  \n  February 25, 2026\n  \n  11 min read\n  \n  [Machine learning](https:\/\/www.amazon.science\/research-areas\/machine-learning)\n\n[View all](https:\/\/www.amazon.science\/blog)\n\n## Featured news\n[![ARA-CFP-Spring26-01-16x9.png](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/www.amazon.science\/research-awards\/latest-news\/amazon-research-awards-issues-spring-2026-call-for-proposals)\n\n[Amazon Research Awards issues Spring CFP](https:\/\/www.amazon.science\/research-awards\/latest-news\/amazon-research-awards-issues-spring-2026-call-for-proposals)\n\nNow open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6\n\n[![FINAL - making a mind Series Image (16x9).png](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/labs.amazon.science\/blog\/new-making-a-mind-podcast-explores-science-of-intelligence)\n\n[“Making a Mind” podcast explores science of intelligence](https:\/\/labs.amazon.science\/blog\/new-making-a-mind-podcast-explores-science-of-intelligence)\n\nHosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.\n\n[![AmazonNovaAIChallenge-logo.jpg](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/www.amazon.science\/nova-ai-challenge\/amazon-announces-the-2026-amazon-nova-ai-challenge-trusted-software-agents)\n\n[2026 Amazon Nova AI Challenge: Trusted Software Agents track](https:\/\/www.amazon.science\/nova-ai-challenge\/amazon-announces-the-2026-amazon-nova-ai-challenge-trusted-software-agents)\n\nChallenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.\n\n[![amazonai-phdfellowship-homepage\\_720.png](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/www.amazon.science\/news\/amazon-launches-68-million-ai-phd-fellowship-program)\n\n[Amazon launches \\$68 million AI PhD Fellowship program](https:\/\/www.amazon.science\/news\/amazon-launches-68-million-ai-phd-fellowship-program)\n\nInitiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.\n\n## Publications\n[View all](https:\/\/www.amazon.science\/publications) [View all](https:\/\/www.amazon.science\/publications)\n\n- [Encoding domain expertise in agents: Lessons from NFL Fantasy AI](https:\/\/www.amazon.science\/publications\/encoding-domain-expertise-in-agents-lessons-from-nfl-fantasy-ai)\n  \n  Michael Butler, [Henry Wang](https:\/\/www.amazon.science\/author\/henry-wang), [Jake Lee](https:\/\/www.amazon.science\/author\/jake-lee-1), Kenton Blacut, [Dan Volk](https:\/\/www.amazon.science\/author\/dan-volk), Mike Band, [Diego Socolinsky](https:\/\/www.amazon.science\/author\/diego-socolinsky)\n  \n  ISACE 2026\n  \n  2026\n  \n  Agentic AI systems can access vast data but struggle to apply domain expertise, namely the contextual understanding of how to use specialized information. This paper presents a practical framework for encoding such expertise, demonstrated with the National Football League (NFL) through NFL Fantasy AI, a production system delivering analyst-grade fantasy football advice, as assessed by NFL Pro analysts.\n  \n  [Conversational AI](https:\/\/www.amazon.science\/research-areas\/conversational-ai-natural-language-processing)\n- [Rethinking language models for building outline extraction from remote sensing imagery](https:\/\/www.amazon.science\/publications\/rethinking-language-models-for-building-outline-extraction-from-remote-sensing-imagery)\n  \n  [Will Qian](https:\/\/www.amazon.science\/author\/will-qian), [Yang He](https:\/\/www.amazon.science\/author\/yang-he), [Mohamed Moustafa](https:\/\/www.amazon.science\/author\/mohamed-moustafa)\n  \n  CVPR 2026 EarthVision Workshop\n  \n  2026\n  \n  Building outline extraction from remote sensing imagery traditionally relies on segmentation or detection followed by post-processing to derive polygonal geometries. Despite advances in sequential prediction methods \\[2, 20\\], end-to-end extraction remains challenging, often missing buildings or requiring additional refinement steps. In this work, we reformulate building outline extraction as next-coordinate\n  \n  [Computer vision](https:\/\/www.amazon.science\/research-areas\/computer-vision)\n- [A framework for prompt optimization and translation across foundation models](https:\/\/www.amazon.science\/publications\/a-framework-for-prompt-optimization-and-translation-across-foundation-models)\n  \n  Abhinav Shankaranarayanan Venkataraman, [Thanos Nikolakopoulos](https:\/\/www.amazon.science\/author\/thanos-nikolakopoulos), Vishwanath Kumaraswamy, [Tao Zhang](https:\/\/www.amazon.science\/author\/tao-zhang), Sarath Chander, Rohit Saboo, [Suleiman Khan](https:\/\/www.amazon.science\/author\/suleiman-khan)\n  \n  ICLR 2026 Workshop on AI with Recursive Self-Improvement\n  \n  2026\n  \n  Foundation-model upgrades frequently break deployed prompt-based systems: target models differ in chat-template conventions, multimodal interfaces, context limits, and structured-output reliability. We study cross-model prompt adaptation: given a prompt program validated on a source model, produce a target-model prompt that preserves a semantic contract and an interface contract under bounded regression\n  \n  [Automated reasoning](https:\/\/www.amazon.science\/research-areas\/automated-reasoning)\n- [Query-specific causal graph pruning under tiered knowledge](https:\/\/www.amazon.science\/publications\/query-specific-causal-graph-pruning-under-tiered-knowledge)\n  \n  Yizuo Chen, [Jane Barker](https:\/\/www.amazon.science\/author\/jane-barker)\n  \n  [ICLR 2026](https:\/\/www.amazon.science\/conferences-and-events\/iclr-2026)\n  \n  2026\n  \n  We present a systematic method for pruning edges from causal graphs by leveraging tiered knowledge. We characterize conditions under which edges can be removed from a causal graph while preserving the identifiability of (conditional) causal effects. This result enables causal identification on simplified graphs that are substantially smaller than the original graphs. The approach is particularly valuable\n  \n  [Economics](https:\/\/www.amazon.science\/research-areas\/economics)\n- [MuonBP: Faster Muon via block-periodic orthogonalization](https:\/\/www.amazon.science\/publications\/muonbp-faster-muon-via-block-periodic-orthogonalization)\n  \n  [Ahmed Khaled](https:\/\/www.amazon.science\/author\/ahmed-khaled), [Kaan Ozkara](https:\/\/www.amazon.science\/author\/ufuk-kaan-ozkara), [Tao Yu](https:\/\/www.amazon.science\/author\/tao-yu), [Mingyi Hong](https:\/\/www.amazon.science\/author\/mingyi-hong), [Youngsuk Park](https:\/\/www.amazon.science\/author\/youngsuk-park)\n  \n  [ICLR 2026](https:\/\/www.amazon.science\/conferences-and-events\/iclr-2026)\n  \n  2026\n  \n  Gradient orthogonalization is a simple strategy that shows great utility in speeding up gradient descent. The Muon optimizer (Jordan et al., 2024b) combines gradient orthogonalization with first-order momentum and achieves significant improvement in data efficiency over Adam\/AdamW (Loshchilov & Hutter, 2019a) for language model training. However, when using model parallelism, gradient orthogonalization\n  \n  [Machine learning](https:\/\/www.amazon.science\/research-areas\/machine-learning)\n\n[Load more](https:\/\/www.amazon.science\/?0000016e-8f90-d381-abee-cfb5f49d0000-page=2)\n\n## Collaborations\n[View all](https:\/\/www.amazon.science\/academic-engagements)\n\nWhether you're a faculty member or student, there are number of ways you can engage with Amazon.\n\n[View all](https:\/\/www.amazon.science\/academic-engagements)\n\n[![Russ Tedrake (Massachusetts Institute of Technology).JPG](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/www.amazon.science\/research-awards)\n\nGretchen Ertl\n\n[Amazon Research Awards](https:\/\/www.amazon.science\/research-awards)\n\nThe program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.\n\n[![AI Challenge-178.jpg](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/www.amazon.science\/nova-ai-challenge)\n\n[Amazon Nova AI Challenge](https:\/\/www.amazon.science\/nova-ai-challenge)\n\nA global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.\n\n[![Tübingen research center ](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/www.amazon.science\/academic-engagements\/research-collaborations)\n\nCredit: Wolfram Scheible\n\n[Research collaborations](https:\/\/www.amazon.science\/academic-engagements\/research-collaborations)\n\nWe partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.\n\n[![Pai-Ling Yin, senior manager of research science, is seen speaking to a classroom, there is a chalkboard behind her and she is gesturing with her hands.](data:image\/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https:\/\/www.amazon.science\/scholars)\n\nCourtesy of Pai-Ling Yin\n\n[Amazon Scholars](https:\/\/www.amazon.science\/scholars)\n\nWe hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.\n\n[View all](https:\/\/www.amazon.science\/academic-engagements)\n\n## Work with us\n[See more jobs](https:\/\/www.amazon.science\/careers) [See more jobs](https:\/\/www.amazon.science\/careers)\n\n[Data Scientist II, Amazon Business](https:\/\/www.amazon.jobs\/jobs\/10390118\/data-scientist-ii-amazon-business?cmpid=bsp-amazon-science)\n\nUS, WA, Seattle\n\nCome be a part of a rapidly expanding \\$35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation\n\n[Comm Systems Engineer, Wireless Systems](https:\/\/www.amazon.jobs\/jobs\/10389969\/comm-systems-engineer-wireless-systems-?cmpid=bsp-amazon-science)\n\nUS, TX, Austin\n\nAmazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.\n\n[Applied Scientist II, Industrial Robotics Group](https:\/\/www.amazon.jobs\/jobs\/10390552\/applied-scientist-ii-industrial-robotics-group?cmpid=bsp-amazon-science)\n\nUS, MA, N.reading\n\nAmazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Work on design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation\n\n[Senior Applied Scientist, Fauna](https:\/\/www.amazon.jobs\/jobs\/10390491\/senior-applied-scientist-fauna?cmpid=bsp-amazon-science)\n\nUS, NY, New York\n\nWe are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers\n\n[Senior Applied Scientist, Applied AI](https:\/\/www.amazon.jobs\/jobs\/10388549\/senior-applied-scientist-applied-ai?cmpid=bsp-amazon-science)\n\nUS, WA, Bellevue\n\nWe are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals.\n\n[Senior Applied Scientist , Buyer Risk Prevention (BRP)](https:\/\/www.amazon.jobs\/jobs\/10388390\/senior-applied-scientist--buyer-risk-prevention-brp?cmpid=bsp-amazon-science)\n\nIN, KA, Bengaluru\n\nDo you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI\/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions\n\n[Sr. Applied Scientist, Sponsored Products Off-Search Sourcing](https:\/\/www.amazon.jobs\/jobs\/10389212\/sr-applied-scientist-sponsored-products-offsearch-sourcing?cmpid=bsp-amazon-science)\n\nUS, CA, Palo Alto\n\nThe Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and\/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and\/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and\/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond\\!\n\n[Applied Scientist, Artificial General Intelligence, AGI Data Services](https:\/\/www.amazon.jobs\/jobs\/10389047\/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science)\n\nUS, MA, Boston\n\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.\n\n[Applied Scientist, Artificial General Intelligence, AGI Data Services](https:\/\/www.amazon.jobs\/jobs\/10389048\/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science)\n\nUS, MA, Boston\n\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.\n\n[Applied Scientist, Artificial General Intelligence, AGI Data Services](https:\/\/www.amazon.jobs\/jobs\/10389049\/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science)\n\nUS, MA, Boston\n\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.\n\n[See more jobs](https:\/\/www.amazon.science\/careers)\n\n[![Amazon Science](https:\/\/cdn.amazon.science\/ce\/51\/a0ecec814b0894e11c0f22dbcc17\/amazonscience-white.svg)](https:\/\/www.amazon.science\/)\n\n- [About](https:\/\/www.amazon.science\/about)\n- [Research areas](https:\/\/www.amazon.science\/research-areas)\n- [Blog](https:\/\/www.amazon.science\/blog)\n- [Publications](https:\/\/www.amazon.science\/publications)\n- [Conferences](https:\/\/www.amazon.science\/conferences-and-events)\n- [Code and datasets](https:\/\/www.amazon.science\/code-and-datasets)\n- [Academia](https:\/\/www.amazon.science\/academic-engagements)\n- [Amazon News](https:\/\/www.aboutamazon.com\/?utm_source=amazon_science&utm_medium=footer&utm_campaign=evergreen_homepage)\n- [Amazon Developer](https:\/\/developer.amazon.com\/)\n- [Amazon Web Services](https:\/\/aws.amazon.com\/blogs)\n- [Awards and recognitions](https:\/\/www.amazon.science\/awards-and-recognitions)\n- [Newsletter](https:\/\/www.linkedin.com\/newsletters\/7185109507369226240\/)\n- [Careers](https:\/\/www.amazon.science\/careers)\n- [FAQs](https:\/\/www.amazon.science\/faqs)\n\n![View from space of a connected network around planet Earth representing the Internet of Things.](https:\/\/cdn.amazon.science\/dims4\/default\/8c95e42\/2147483647\/strip\/true\/crop\/1465x1277+227+0\/resize\/70x61!\/quality\/90\/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F20%2Fc4%2Fc36de9f643dab18e2f27ea071590%2Famazon-science-newsletter-project-kuiper.jpg)\n\n[Get more from Amazon Science](https:\/\/www.amazon.science\/newsletter)\n\nSubscribe to our newsletter\n\n[Amazon.com](https:\/\/www.amazon.com\/) \\| [Conditions of Use](https:\/\/www.amazon.com\/gp\/help\/customer\/display.html\/ref=footer_cou?ie=UTF8&nodeId=508088) \\| [Privacy](https:\/\/www.amazon.com\/gp\/help\/customer\/display.html?nodeId=468496) \\| © 1996-2026 Amazon.com, Inc. or its affiliates\n\nSocial\n\n- [twitter](https:\/\/x.com\/AmazonScience)\n- [instagram](https:\/\/www.instagram.com\/AmazonScience\/)\n- [youtube](https:\/\/www.youtube.com\/c\/AmazonScience)\n- [facebook](https:\/\/www.facebook.com\/AmazonScience)\n- [linkedin](https:\/\/www.linkedin.com\/showcase\/AmazonScience)\n- [github](https:\/\/github.com\/amazon-science)\n- [rss](https:\/\/www.amazon.science\/index.rss)","attrs_readable_markdown":"[Data Scientist II, Amazon Business](https:\/\/www.amazon.jobs\/jobs\/10390118\/data-scientist-ii-amazon-business?cmpid=bsp-amazon-science)\n\nUS, WA, Seattle\n\nCome be a part of a rapidly expanding \\$35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation\n\n[Comm Systems Engineer, Wireless Systems](https:\/\/www.amazon.jobs\/jobs\/10389969\/comm-systems-engineer-wireless-systems-?cmpid=bsp-amazon-science)\n\nUS, TX, Austin\n\nAmazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.\n\n[Applied Scientist II, Industrial Robotics Group](https:\/\/www.amazon.jobs\/jobs\/10390552\/applied-scientist-ii-industrial-robotics-group?cmpid=bsp-amazon-science)\n\nUS, MA, N.reading\n\nAmazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Work on design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation\n\n[Senior Applied Scientist, Fauna](https:\/\/www.amazon.jobs\/jobs\/10390491\/senior-applied-scientist-fauna?cmpid=bsp-amazon-science)\n\nUS, NY, New York\n\nWe are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers\n\n[Senior Applied Scientist, Applied AI](https:\/\/www.amazon.jobs\/jobs\/10388549\/senior-applied-scientist-applied-ai?cmpid=bsp-amazon-science)\n\nUS, WA, Bellevue\n\nWe are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals.\n\n[Senior Applied Scientist , Buyer Risk Prevention (BRP)](https:\/\/www.amazon.jobs\/jobs\/10388390\/senior-applied-scientist--buyer-risk-prevention-brp?cmpid=bsp-amazon-science)\n\nIN, KA, Bengaluru\n\nDo you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI\/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions\n\n[Sr. Applied Scientist, Sponsored Products Off-Search Sourcing](https:\/\/www.amazon.jobs\/jobs\/10389212\/sr-applied-scientist-sponsored-products-offsearch-sourcing?cmpid=bsp-amazon-science)\n\nUS, CA, Palo Alto\n\nThe Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and\/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and\/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and\/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond\\!\n\n[Applied Scientist, Artificial General Intelligence, AGI Data Services](https:\/\/www.amazon.jobs\/jobs\/10389047\/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science)\n\nUS, MA, Boston\n\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.\n\n[Applied Scientist, Artificial General Intelligence, AGI Data Services](https:\/\/www.amazon.jobs\/jobs\/10389048\/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science)\n\nUS, MA, Boston\n\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.\n\n[Applied Scientist, Artificial General Intelligence, AGI Data Services](https:\/\/www.amazon.jobs\/jobs\/10389049\/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science)\n\nUS, MA, Boston\n\nThe Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.","meta_canonical":null}

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Boilerpipe Text	Data Scientist II, Amazon Business US, WA, Seattle Come be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation Comm Systems Engineer, Wireless Systems US, TX, Austin Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Applied Scientist II, Industrial Robotics Group US, MA, N.reading Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Work on design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation Senior Applied Scientist, Fauna US, NY, New York We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers Senior Applied Scientist, Applied AI US, WA, Bellevue We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals. Senior Applied Scientist , Buyer Risk Prevention (BRP) IN, KA, Bengaluru Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions Sr. Applied Scientist, Sponsored Products Off-Search Sourcing US, CA, Palo Alto The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Applied Scientist, Artificial General Intelligence, AGI Data Services US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. Applied Scientist, Artificial General Intelligence, AGI Data Services US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. Applied Scientist, Artificial General Intelligence, AGI Data Services US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
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[University of Illinois Urbana-Champaign](https://www.amazon.science/news-and-features/amazon-launches-the-aice-center-at-the-university-of-illinois-urbana-champaign) - [University of Southern California](https://www.amazon.science/academic-engagements/usc-and-amazon-establish-center-for-secure-and-trusted-machine-learning) - [University of Texas at Austin](https://www.amazon.science/news-and-features/amazon-and-university-of-texas-at-austin-launch-science-hub) - [Virginia Tech](https://www.amazon.science/academic-engagements/amazon-and-virginia-tech-launch-ai-and-ml-research-initiative) - [University of Washington](https://www.amazon.science/academic-engagements/new-uw-amazon-science-hub-launches) - Resources - - [Code and datasets](https://www.amazon.science/code-and-datasets) - [AGI Labs Meet the team building useful AI agents.](https://labs.amazon.science/) - [Amazon Nova Try Amazon’s frontier foundation models.](https://nova.amazon.com/) - - [Code and datasets](https://www.amazon.science/code-and-datasets) - [AGI Labs Meet the team building useful AI agents.](https://labs.amazon.science/) - [Amazon Nova Try Amazon’s frontier foundation models.](https://nova.amazon.com/) - [Careers](https://www.amazon.science/careers) - - [Careers Explore our open roles.](https://www.amazon.science/careers) - [Amazon Scholars Faculty research opportunities on industry-scale technical challenges.](https://www.amazon.science/scholars) - [Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists.](https://www.amazon.science/postdoctoral-science-program) - - [Careers Explore our open roles.](https://www.amazon.science/careers) - [Amazon Scholars Faculty research opportunities on industry-scale technical challenges.](https://www.amazon.science/scholars) - [Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists.](https://www.amazon.science/postdoctoral-science-program) ![RLGyms02-Option02-Detailed-Homepage.jpg](https://cdn.amazon.science/dims4/default/b44aaff/2147483647/strip/true/crop/1920x1080+0+0/resize/1440x810!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fd9%2F9e%2Fe4e1c24b47ada232d516b5d10d70%2Frlgyms02-option02-detailed-homepage.jpg) \` [It looks like a simple form. It's actually 40 years of software.](https://www.amazon.science/blog/how-agentic-ai-helps-heal-the-systems-we-cant-replace) Adding a pet to your flight sounds like a one-click task, but every click passes through layers of software dating back to the 1960s. Amazon's AGI Lab trains AI agents not to replace these brittle systems but to learn them deeply enough to finally make them work. [Read more](https://www.amazon.science/blog/how-agentic-ai-helps-heal-the-systems-we-cant-replace) ![TextToSpeech-16x9.gif](https://cdn.amazon.science/dims4/default/cf44c6a/2147483647/strip/true/crop/1280x720+0+0/resize/1440x810!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2Fa0%2F98%2F78f89609444293f87d8acc871c47%2Ftexttospeech-16x9.gif) \` [Improving quality and robustness in LLM-based text-to-speech systems](https://www.amazon.science/blog/improving-quality-and-robustness-in-llm-based-text-to-speech-systems) Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis. [Read more](https://www.amazon.science/blog/improving-quality-and-robustness-in-llm-based-text-to-speech-systems) ![MathResearch-01-16x9.png](https://cdn.amazon.science/dims4/default/b715d5c/2147483647/strip/true/crop/1920x1080+0+0/resize/1440x810!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F37%2F23%2Fb207cbc54257bba9555bd6824c15%2Fmathresearch-01-16x9.png) \` [How AI is changing the nature of mathematical research](https://www.amazon.science/blog/how-ai-is-changing-the-nature-of-mathematical-research) What machine learning theorists learned using AI agents to generate proofs — and what comes next. [Read more](https://www.amazon.science/blog/how-ai-is-changing-the-nature-of-mathematical-research) ![HumanAI-01-16x9.png](https://cdn.amazon.science/dims4/default/09b6850/2147483647/strip/true/crop/1920x1080+0+0/resize/1440x810!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F5f%2Ffd%2Fbf22bfe241c3ac6d001d34277eef%2Fhumanai-01-16x9.png) \` [Designing AI agents that know when to step back](https://www.amazon.science/blog/designing-ai-agents-that-know-when-to-step-back) As AI agents become more autonomous, the key challenge isn't what they can do; it's how to design the human side of the equation. [Read more](https://www.amazon.science/blog/designing-ai-agents-that-know-when-to-step-back) It looks like a simple form. It's actually 40 years of software. Improving quality and robustness in LLM-based text-to-speech systems How AI is changing the nature of mathematical research Designing AI agents that know when to step back # Customer-obsessed science ![Amazon Science homepage.jpeg](https://cdn.amazon.science/dims4/default/39c1939/2147483647/strip/true/crop/1385x1200+208+0/resize/435x377!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F48%2F0f%2F1db2f1004b82a99a0175ff391d53%2Famazon-science-homepage.jpeg) ![EchoFrame\_Animated\_121124 (1).gif](https://cdn.amazon.science/dims4/default/db48e94/2147483647/strip/true/crop/646x563+177+0/resize/218x190!/quality/90/?url=https%3A%2F%2Famzn-science-production-science.s3.us-east-1.amazonaws.com%2Fscience%2F8a%2F79%2Fad103fb544aaa2fbdf0745c366f1%2Fechoframe-animated-121124-1.gif) ## Research areas [![AutomatedReasoning.svg]() Automated reasoning](https://www.amazon.science/research-areas/automated-reasoning) [![Cloud.svg]() Cloud and systems](https://www.amazon.science/research-areas/cloud-and-systems) [![ComputerVision.svg]() Computer vision](https://www.amazon.science/research-areas/computer-vision) [![ConversationalAI.svg]() Conversational AI](https://www.amazon.science/research-areas/conversational-ai-natural-language-processing) [![Economics.svg]() Economics](https://www.amazon.science/research-areas/economics) [![InfoManagement.svg]() Information and knowledge management](https://www.amazon.science/research-areas/information-and-knowledge-management) [![MachineLearning.svg]() Machine learning](https://www.amazon.science/research-areas/machine-learning) [![Operations.svg]() Operations research and optimization](https://www.amazon.science/research-areas/operations-research-and-optimization) [![Quantum Technologies.svg]() Quantum technologies](https://www.amazon.science/research-areas/quantum-technologies) [![Robotics.svg]() Robotics](https://www.amazon.science/research-areas/robotics) [![Search.svg]() Search and information retrieval](https://www.amazon.science/research-areas/search-and-information-retrieval) [![Security.svg]() Security, privacy, and abuse prevention](https://www.amazon.science/research-areas/security-privacy-and-abuse-prevention) [![Sustainability.svg]() Sustainability](https://www.amazon.science/research-areas/sustainability) ## From the blog [View all](https://www.amazon.science/blog) Technical deep-dives and perspectives from our scientists. [View all](https://www.amazon.science/blog) - [![generation2.jpg](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMzdweCIgd2lkdGg9IjYwMHB4Ij48L3N2Zz4=)](https://www.amazon.science/blog/how-amazon-uses-agentic-ai-for-vulnerability-detection-at-global-scale) [How Amazon uses agentic AI for vulnerability detection at global scale](https://www.amazon.science/blog/how-amazon-uses-agentic-ai-for-vulnerability-detection-at-global-scale) April 8, 2026 6 min read Amazon’s RuleForge system uses agentic AI to generate production-ready detection rules 336% faster than traditional methods. [Security, privacy, and abuse prevention](https://www.amazon.science/research-areas/security-privacy-and-abuse-prevention) - [![mlkem-native v4.JPG](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https://www.amazon.science/blog/verifying-and-optimizing-post-quantum-cryptography-at-amazon) [Verifying and optimizing post-quantum cryptography at Amazon](https://www.amazon.science/blog/verifying-and-optimizing-post-quantum-cryptography-at-amazon) April 7, 2026 13 min read [Automated reasoning](https://www.amazon.science/research-areas/automated-reasoning) - [![The basic XEX block encryption structure with the XOR-Encrypt-XOR flow v3.jpg](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https://www.amazon.science/blog/formally-verified-aes-xts-the-first-aes-algorithm-to-join-s2n-bignum) [Formally verified AES-XTS: The first AES algorithm to join s2n-bignum](https://www.amazon.science/blog/formally-verified-aes-xts-the-first-aes-algorithm-to-join-s2n-bignum) March 20, 2026 15 min read [Automated reasoning](https://www.amazon.science/research-areas/automated-reasoning) - [![LoRA.16x9.png](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https://www.amazon.science/blog/optimizing-lora-target-module-selection-for-efficient-fine-tuning) [Optimizing LoRA target module selection for efficient fine tuning](https://www.amazon.science/blog/optimizing-lora-target-module-selection-for-efficient-fine-tuning) March 19, 2026 11 min read [Machine learning](https://www.amazon.science/research-areas/machine-learning) - [![ZebraTime-16x9-01.png](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIyNDlweCIgd2lkdGg9IjQ0MnB4Ij48L3N2Zz4=)](https://www.amazon.science/blog/intelligence-isnt-about-parameter-count-its-about-time) [Intelligence isn’t about parameter count. It’s about time.](https://www.amazon.science/blog/intelligence-isnt-about-parameter-count-its-about-time) February 25, 2026 11 min read [Machine learning](https://www.amazon.science/research-areas/machine-learning) [View all](https://www.amazon.science/blog) ## Featured news [![ARA-CFP-Spring26-01-16x9.png](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://www.amazon.science/research-awards/latest-news/amazon-research-awards-issues-spring-2026-call-for-proposals) [Amazon Research Awards issues Spring CFP](https://www.amazon.science/research-awards/latest-news/amazon-research-awards-issues-spring-2026-call-for-proposals) Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6 [![FINAL - making a mind Series Image (16x9).png](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://labs.amazon.science/blog/new-making-a-mind-podcast-explores-science-of-intelligence) [“Making a Mind” podcast explores science of intelligence](https://labs.amazon.science/blog/new-making-a-mind-podcast-explores-science-of-intelligence) Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence. [![AmazonNovaAIChallenge-logo.jpg](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://www.amazon.science/nova-ai-challenge/amazon-announces-the-2026-amazon-nova-ai-challenge-trusted-software-agents) [2026 Amazon Nova AI Challenge: Trusted Software Agents track](https://www.amazon.science/nova-ai-challenge/amazon-announces-the-2026-amazon-nova-ai-challenge-trusted-software-agents) Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale. [![amazonai-phdfellowship-homepage\_720.png](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://www.amazon.science/news/amazon-launches-68-million-ai-phd-fellowship-program) [Amazon launches \$68 million AI PhD Fellowship program](https://www.amazon.science/news/amazon-launches-68-million-ai-phd-fellowship-program) Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities. ## Publications [View all](https://www.amazon.science/publications) [View all](https://www.amazon.science/publications) - [Encoding domain expertise in agents: Lessons from NFL Fantasy AI](https://www.amazon.science/publications/encoding-domain-expertise-in-agents-lessons-from-nfl-fantasy-ai) Michael Butler, [Henry Wang](https://www.amazon.science/author/henry-wang), [Jake Lee](https://www.amazon.science/author/jake-lee-1), Kenton Blacut, [Dan Volk](https://www.amazon.science/author/dan-volk), Mike Band, [Diego Socolinsky](https://www.amazon.science/author/diego-socolinsky) ISACE 2026 2026 Agentic AI systems can access vast data but struggle to apply domain expertise, namely the contextual understanding of how to use specialized information. This paper presents a practical framework for encoding such expertise, demonstrated with the National Football League (NFL) through NFL Fantasy AI, a production system delivering analyst-grade fantasy football advice, as assessed by NFL Pro analysts. [Conversational AI](https://www.amazon.science/research-areas/conversational-ai-natural-language-processing) - [Rethinking language models for building outline extraction from remote sensing imagery](https://www.amazon.science/publications/rethinking-language-models-for-building-outline-extraction-from-remote-sensing-imagery) [Will Qian](https://www.amazon.science/author/will-qian), [Yang He](https://www.amazon.science/author/yang-he), [Mohamed Moustafa](https://www.amazon.science/author/mohamed-moustafa) CVPR 2026 EarthVision Workshop 2026 Building outline extraction from remote sensing imagery traditionally relies on segmentation or detection followed by post-processing to derive polygonal geometries. Despite advances in sequential prediction methods \[2, 20\], end-to-end extraction remains challenging, often missing buildings or requiring additional refinement steps. In this work, we reformulate building outline extraction as next-coordinate [Computer vision](https://www.amazon.science/research-areas/computer-vision) - [A framework for prompt optimization and translation across foundation models](https://www.amazon.science/publications/a-framework-for-prompt-optimization-and-translation-across-foundation-models) Abhinav Shankaranarayanan Venkataraman, [Thanos Nikolakopoulos](https://www.amazon.science/author/thanos-nikolakopoulos), Vishwanath Kumaraswamy, [Tao Zhang](https://www.amazon.science/author/tao-zhang), Sarath Chander, Rohit Saboo, [Suleiman Khan](https://www.amazon.science/author/suleiman-khan) ICLR 2026 Workshop on AI with Recursive Self-Improvement 2026 Foundation-model upgrades frequently break deployed prompt-based systems: target models differ in chat-template conventions, multimodal interfaces, context limits, and structured-output reliability. We study cross-model prompt adaptation: given a prompt program validated on a source model, produce a target-model prompt that preserves a semantic contract and an interface contract under bounded regression [Automated reasoning](https://www.amazon.science/research-areas/automated-reasoning) - [Query-specific causal graph pruning under tiered knowledge](https://www.amazon.science/publications/query-specific-causal-graph-pruning-under-tiered-knowledge) Yizuo Chen, [Jane Barker](https://www.amazon.science/author/jane-barker) [ICLR 2026](https://www.amazon.science/conferences-and-events/iclr-2026) 2026 We present a systematic method for pruning edges from causal graphs by leveraging tiered knowledge. We characterize conditions under which edges can be removed from a causal graph while preserving the identifiability of (conditional) causal effects. This result enables causal identification on simplified graphs that are substantially smaller than the original graphs. The approach is particularly valuable [Economics](https://www.amazon.science/research-areas/economics) - [MuonBP: Faster Muon via block-periodic orthogonalization](https://www.amazon.science/publications/muonbp-faster-muon-via-block-periodic-orthogonalization) [Ahmed Khaled](https://www.amazon.science/author/ahmed-khaled), [Kaan Ozkara](https://www.amazon.science/author/ufuk-kaan-ozkara), [Tao Yu](https://www.amazon.science/author/tao-yu), [Mingyi Hong](https://www.amazon.science/author/mingyi-hong), [Youngsuk Park](https://www.amazon.science/author/youngsuk-park) [ICLR 2026](https://www.amazon.science/conferences-and-events/iclr-2026) 2026 Gradient orthogonalization is a simple strategy that shows great utility in speeding up gradient descent. The Muon optimizer (Jordan et al., 2024b) combines gradient orthogonalization with first-order momentum and achieves significant improvement in data efficiency over Adam/AdamW (Loshchilov & Hutter, 2019a) for language model training. However, when using model parallelism, gradient orthogonalization [Machine learning](https://www.amazon.science/research-areas/machine-learning) [Load more](https://www.amazon.science/?0000016e-8f90-d381-abee-cfb5f49d0000-page=2) ## Collaborations [View all](https://www.amazon.science/academic-engagements) Whether you're a faculty member or student, there are number of ways you can engage with Amazon. [View all](https://www.amazon.science/academic-engagements) [![Russ Tedrake (Massachusetts Institute of Technology).JPG](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://www.amazon.science/research-awards) Gretchen Ertl [Amazon Research Awards](https://www.amazon.science/research-awards) The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission. [![AI Challenge-178.jpg](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://www.amazon.science/nova-ai-challenge) [Amazon Nova AI Challenge](https://www.amazon.science/nova-ai-challenge) A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security. [![Tübingen research center ](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://www.amazon.science/academic-engagements/research-collaborations) Credit: Wolfram Scheible [Research collaborations](https://www.amazon.science/academic-engagements/research-collaborations) We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest. [![Pai-Ling Yin, senior manager of research science, is seen speaking to a classroom, there is a chalkboard behind her and she is gesturing with her hands.](data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSI2OTVweCIgd2lkdGg9IjEyNDBweCI+PC9zdmc+)](https://www.amazon.science/scholars) Courtesy of Pai-Ling Yin [Amazon Scholars](https://www.amazon.science/scholars) We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities. [View all](https://www.amazon.science/academic-engagements) ## Work with us [See more jobs](https://www.amazon.science/careers) [See more jobs](https://www.amazon.science/careers) [Data Scientist II, Amazon Business](https://www.amazon.jobs/jobs/10390118/data-scientist-ii-amazon-business?cmpid=bsp-amazon-science) US, WA, Seattle Come be a part of a rapidly expanding \$35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation [Comm Systems Engineer, Wireless Systems](https://www.amazon.jobs/jobs/10389969/comm-systems-engineer-wireless-systems-?cmpid=bsp-amazon-science) US, TX, Austin Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. [Applied Scientist II, Industrial Robotics Group](https://www.amazon.jobs/jobs/10390552/applied-scientist-ii-industrial-robotics-group?cmpid=bsp-amazon-science) US, MA, N.reading Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Work on design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation [Senior Applied Scientist, Fauna](https://www.amazon.jobs/jobs/10390491/senior-applied-scientist-fauna?cmpid=bsp-amazon-science) US, NY, New York We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers [Senior Applied Scientist, Applied AI](https://www.amazon.jobs/jobs/10388549/senior-applied-scientist-applied-ai?cmpid=bsp-amazon-science) US, WA, Bellevue We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals. [Senior Applied Scientist , Buyer Risk Prevention (BRP)](https://www.amazon.jobs/jobs/10388390/senior-applied-scientist--buyer-risk-prevention-brp?cmpid=bsp-amazon-science) IN, KA, Bengaluru Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions [Sr. Applied Scientist, Sponsored Products Off-Search Sourcing](https://www.amazon.jobs/jobs/10389212/sr-applied-scientist-sponsored-products-offsearch-sourcing?cmpid=bsp-amazon-science) US, CA, Palo Alto The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond\! [Applied Scientist, Artificial General Intelligence, AGI Data Services](https://www.amazon.jobs/jobs/10389047/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science) US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. [Applied Scientist, Artificial General Intelligence, AGI Data Services](https://www.amazon.jobs/jobs/10389048/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science) US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. [Applied Scientist, Artificial General Intelligence, AGI Data Services](https://www.amazon.jobs/jobs/10389049/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science) US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. 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Readable Markdown	[Data Scientist II, Amazon Business](https://www.amazon.jobs/jobs/10390118/data-scientist-ii-amazon-business?cmpid=bsp-amazon-science) US, WA, Seattle Come be a part of a rapidly expanding \$35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation [Comm Systems Engineer, Wireless Systems](https://www.amazon.jobs/jobs/10389969/comm-systems-engineer-wireless-systems-?cmpid=bsp-amazon-science) US, TX, Austin Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. [Applied Scientist II, Industrial Robotics Group](https://www.amazon.jobs/jobs/10390552/applied-scientist-ii-industrial-robotics-group?cmpid=bsp-amazon-science) US, MA, N.reading Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Work on design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation [Senior Applied Scientist, Fauna](https://www.amazon.jobs/jobs/10390491/senior-applied-scientist-fauna?cmpid=bsp-amazon-science) US, NY, New York We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers [Senior Applied Scientist, Applied AI](https://www.amazon.jobs/jobs/10388549/senior-applied-scientist-applied-ai?cmpid=bsp-amazon-science) US, WA, Bellevue We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for a Senior Applied Scientist to join our Applied AI team to work on LLM-based solutions. On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals. [Senior Applied Scientist , Buyer Risk Prevention (BRP)](https://www.amazon.jobs/jobs/10388390/senior-applied-scientist--buyer-risk-prevention-brp?cmpid=bsp-amazon-science) IN, KA, Bengaluru Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions [Sr. Applied Scientist, Sponsored Products Off-Search Sourcing](https://www.amazon.jobs/jobs/10389212/sr-applied-scientist-sponsored-products-offsearch-sourcing?cmpid=bsp-amazon-science) US, CA, Palo Alto The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond\! [Applied Scientist, Artificial General Intelligence, AGI Data Services](https://www.amazon.jobs/jobs/10389047/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science) US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. [Applied Scientist, Artificial General Intelligence, AGI Data Services](https://www.amazon.jobs/jobs/10389048/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science) US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice. [Applied Scientist, Artificial General Intelligence, AGI Data Services](https://www.amazon.jobs/jobs/10389049/applied-scientist-artificial-general-intelligence-agi-data-services?cmpid=bsp-amazon-science) US, MA, Boston The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
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