Careers

Amazon's PRISM team is seeking an innovative Applied Scientist to build the intelligence layer powering the Catalog Diagnostic Assistant — a conversational AI agent that unifies Amazon's fragmented catalog diagnostic experience into a single natural-language interface. Rather than switching between legacy tools, internal users ask CDA a question in plain language and CDA coordinates across data sources, reasons through complex diagnostic workflows, and returns a combined answer with source citations. This role sits at the intersection of Generative AI, agentic architectures, and large-scale information retrieval applied to the world's largest product catalog. You will design and build the scientific core of an agent that autonomously investigates catalog anomalies — diagnosing why products aren't live, why attributes aren't publishing, or why matching decisions went wrong — across billions of products, petabytes of multimodal data, and dozens of marketplaces. You will be the founding scientist for the CDA product, defining the research agenda for agentic diagnostics, developing novel approaches to skill-based reasoning and tool orchestration, and owning the full lifecycle from problem formulation through production deployment at Amazon scale. You will pioneer advanced GenAI solutions that power next-generation agentic experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog and tackle problems at the frontier of AI research. Key job responsibilities - Formulate open research problems at the intersection of GenAI, agentic reasoning, and large-scale catalog diagnostics — defining how an autonomous agent should decompose, investigate, and explain complex catalog issues - Design and develop novel agentic architectures (skill planning, tool selection, multi-step reasoning, chain-of-thought verification) that enable CDA to autonomously resolve diagnostic workflows that traditionally required manual expert investigation - Build and optimize retrieval-augmented generation (RAG) systems over Amazon's catalog data, ensuring the agent retrieves the right evidence from the right data sources to ground its diagnostic answers - Advance the science of efficient model deployment — developing distillation, compression, and LLM serving optimization strategies that preserve diagnostic reasoning quality in production-grade architectures while reducing latency and cost - Make frontier models reliable for autonomous decisions — advancing uncertainty calibration, confidence estimation, and interpretability methods so CDA's agentic diagnoses can be trusted at scale - Own the full research lifecycle from problem formulation through production deployment — designing rigorous experiments, iterating rapidly, and seeing your research directly improve diagnostic accuracy and coverage - Partner closely with CDA engineers to translate research prototypes into production systems serving thousands of daily diagnostic sessions - Shape the team's research vision by defining technical roadmaps that balance foundational scientific inquiry with measurable product impact - Mentor engineers on advanced ML/GenAI techniques, experimental design, and scientific rigor About the team We are a GenAI science team within Amazon's Selection and Catalog Systems (ASCS) organization. Our mission is to advance state-of-the-art Generative AI to deeply understand, uniquely identify, and intelligently diagnose every product at Amazon scale. We push the boundaries of multimodal LLMs, agentic systems, and generative AI to solve foundational catalog challenges — from product identity and semantic matching to automated catalog diagnostics and intelligent catalog analytics. Our scientists work across multiple high-impact problem spaces: establishing canonical product identity across billions of items, powering next-generation agentic experiences for catalog operations, and building AI systems that make Amazon's catalog self-diagnosing and self-healing. We operate at the frontier of AI research applied at unprecedented scale — petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity. This is a collaborative, fast-moving environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your ideas at scale, and publish your findings at top venues.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. Amazon's advertising portfolio helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Millions of advertisers rely on Amazon's self-service support experience to resolve issues, unblock campaigns, and grow their business. Our Support Agents team is building the science behind intelligent, conversational support — systems that understand advertiser intent, retrieve the right knowledge, generate accurate answers, and know when to escalate. We serve ~2M monthly active advertisers across dozens of locales and languages, and every percentage point of improvement in resolution quality translates directly into advertiser success and retention. We are seeking an Applied Scientist to help enhance and scale our support agent capabilities. You will build and improve models that power advertiser-facing interactions, contribute to our evaluation framework, and work alongside senior scientists and engineers to ship science into production. A day in the life 1. Enhance support agent capabilities across the broad suite of Amazon Advertising products — expanding coverage, depth of resolution, and advertiser task completion across Sponsored Products, Sponsored Brands, DSP, AMC, and more 2. Design and own the evaluation framework for agent quality — including automated LLM-based scoring of answer correctness, confidence calibration, and conversation-level resolution signals 3. Develop novel metrics that capture whether advertisers actually got the help they needed (beyond surface-level deflection rates) 4. Build and improve retrieval and generation models that power real-time advertiser interactions under strict latency SLAs 5. Drive multilingual science — improve non-English resolution rates through cross-lingual retrieval, translation quality modeling, and locale-aware evaluation 6. Partner with product, engineering, and business teams to productize research and inform roadmap decisions with data About the team This role sits within Amazon Advertising's broader Agentic Intelligence organization — a community of multiple Applied Science and Engineering teams building the next generation of AI-powered experiences for advertisers. You'll have access to Principal Engineers and Principal Applied Scientists to pressure-test ideas and elevate your work. The team is cross-functional — scientists and engineers work shoulder-to-shoulder, from problem framing through production deployment.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide

Your Next Big Challenge Starts Here You've spent years mastering machine learning. Now imagine applying that expertise where it actually reaches billions of people — every single day. At Amazon Ads, we're not just running ads. We're re-inventing the entire advertising experience using generative AI, large language models, and next-generation ML. From the moment a brand crafts a campaign to the instant a customer discovers something they love — your science powers it all. Billions of impressions. Millions of clicks. Petabytes of data. And you — at the center of it. Why This Role Will Excite You Unmatched scale and resources. You'll have access to computational power and datasets most scientists only dream about. Build solutions that don't just work in a notebook — they work at planet scale. Breadth that keeps you sharp. One quarter you might be optimizing real-time bidding systems. The next, you're building creative AI that generates ad content. Ranking, personalization, NLP, computer vision, LLMs — it's all on the table. Your ideas ship. This isn't a research lab where papers collect dust. You'll take models from concept to production, run A/B experiments, and see your work move metrics that matter — for advertisers, customers, and the business. People who push you forward. You'll collaborate with world-class engineers, scientists, and product leaders who are just as passionate about solving hard problems as you are. What You'll Actually Do Research and build next-generation ML solutions — including generative AI and LLM applications — that transform how advertising works Own end-to-end projects: from messy, ambiguous problems to deployed, production-grade models Design experiments that validate your hypotheses and measure real business impact Build models that balance what's best for customers and advertisers — because great advertising should feel helpful, not intrusive Work cross-functionally with engineers, PMs, and fellow scientists to ship fast and iterate faster Develop scalable ML pipelines that optimize monetization without sacrificing customer experience Where This Takes Your Career Whether you want to go deep as a technical IC or grow into people leadership — both paths are real here. You'll have opportunities to: Lead high-visibility technical initiatives Mentor and grow other scientists Shape strategy alongside senior leadership Build a reputation in a community that genuinely values scientific excellence Your work won't just be seen internally — it'll impact millions of customers and advertisers worldwide. Watch Scientists at Amazon Ads Talk About Their Work https://www.youtube.com/watch?v=vvHsURsIPEA Learn More About Amazon Ads https://advertising.amazon.com/ Basic Qualifications 5+ years building ML models for real business applications PhD, or Master's degree + 6 years of applied research experience Strong programming skills in Python, Java, C++, or similar Hands-on experience with deep learning and neural network methods Preferred Qualifications Experience with tools like scikit-learn, TensorFlow, PyTorch, Spark MLLib, MxNet, numpy, scipy Experience with large-scale distributed systems (Hadoop, Spark, etc.)

This role will develop Economic and Econometric models and thought leadership on projects that span our business, with a particular focus on supply chain and inventory selection. Through the lens of economics, you will develop a deeper understanding of how customers value our selection and interact with Amazon's store to make shopping easier. You will be a key scientist, advising Amazon leaders how to evaluate selection and understand customers. You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policies for Amazon's business. You will partner with science and technology teams across Amazon including Supply Chain, Operations, Retail, FBA, Consumer Pricing, and Finance. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. Ability to incorporate AI tools and methods into your work is highly valued. They will work closely with Economists, Data/Applied Scientists, Software Engineers and Product Managers to integrate economic insights into policy and systems production. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Personalization systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Prime Video Growth Science team owns science solution to power online new content experience and offline experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.

We’re looking for a Research Scientist to join a team that measures and explains how over 2.4 million sellers and vendors experience selling on Amazon. You’ll apply survey science, psychometrics, and applied statistics to help drive meaningful change at Amazon on behalf of Sellers. In this role, you’ll work across a variety of research methodologies to optimize our data collection, create scalable analytical approaches, and deep dive the Seller experience to create rigorous, quantitative insights that senior leaders use to set strategy. Key job responsibilities Key Job Responsibilities - Apply psychometric and survey methodology techniques (e.g., IRT, factor analysis, scale development, single-item indicators) to measure seller experience constructs with scientific rigor - Contribute to frameworks that link seller attitudinal data to behavioral outcomes and identify high-impact opportunity areas - Design and execute statistical analyses including regression modeling, significance testing, and driver analysis to identify what matters most to sellers - Apply observational causal evaluation methods to estimate the effects of policy changes, product launches, and platform interventions on seller experience - Build and maintain analytical pipelines that transform raw survey data into production-ready metrics, reports, and dashboards - Analyze open-ended survey responses using text classification, thematic coding, and natural language processing techniques - Monitor and improve survey response rates, sampling methodology, and data quality - Productionalize research code: take analyses from prototype to automated, reproducible pipelines that run reliably in production environments - Communicate findings clearly to technical and non-technical audiences through written reports, data visualizations, and presentations - Collaborate with cross-functional partners to translate business questions into well-defined research problems and scientific metrics - Document research methods, assumptions, and limitations transparently to ensure reproducibility A day in the life Your day typically starts with the data. You might spend the morning reviewing satisfaction trends, investigating a shift in a key metric, and pulling together an analysis that explains what’s driving it. You’ll regularly meet with external teams to help them understand how a proposed product will affect seller sentiment and what the data says they should prioritize. You’ll also spend time in R or Python building, training, or testing models to improve how we measure and act on sentiment data. About the team Our team owns the research and measurement infrastructure that tracks satisfaction across all 2.1 million selling partners on Amazon, spanning Seller Central, Next Gen Selling, and Mobile. We sit at the intersection of data and strategy, partnering with teams across product, design, and engineering to advocate for seller experience improvements. This is a high-visibility team where the work is consequential, the stakeholders are senior, and the problems are genuinely hard.

Are you passionate about leveraging your applied science skills to deliver actionable insights that impact daily business decisions? Do you like using ML, GenAI, Causal Inference, Reinforcement Learning and Experimentation to answer challenging product and customer behavior questions? Do you want to be a technical leader and build flexible and global solutions for complex financial services/payments, risk, and causal problems? If so, here is a great opportunity to consider! Amazon B2B Payments & Lending is seeking an Applied Scientist II who will combine their technical expertise with business acumen to generate critical insights that will set the strategic direction of the business. You will be a thought leader on the team, help set the team's strategic focus and roadmaps, and design and build systems/solutions that support financial products, working closely with business/product partners and engineers. You will utilize ML/GenAI/Causal Inference/Reinforcement Learning/Experimentation methodologies, data and coding skills, problem solving and analytical skills, and excellent communication to deliver customer value. Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

The Amazon Robotics team is seeking an experienced Applied Scientist to join our team. In this role you will apply the latest trends in research to solve real-world problems in robotics and AI. You will collaborate with a team of scientists and engineers building these applications. We holistically design, build, and deliver end-to-end robotic systems. Our team is also responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, machine learning scientists, software engineers, and hardware engineers to collaborate and deploy systems in the field. Key job responsibilities • Research, design, implement and evaluate complex perception, motion planning, and decision making algorithms integrating across multiple disciplines and leveraging machine learning. • Create experiments and prototype implementations of new learning algorithms and prediction techniques. • Work closely with software engineering team members to drive scalable, real-time implementations. • Collaborate with machine learning and robotic controls experts to implement and deploy algorithms, such as machine learning models. • Collaborate closely with hardware engineering team members on developing systems from prototyping to production level. • Represent Amazon in academia community through publications and scientific presentations. • Work with stakeholders across hardware, science, and operations teams to iterate on systems design and implementation. About the team Watch this video to learn more about Vulcan Pick team in Amazon Robotics: https://www.amazon.science/publications/vulcan-pick-a-robotic-system-for-picking-targeted-objects-from-fabric-pods

Amazon's Supply Chain is the backbone of the fastest growing e-commerce business in the world, and planning it is one of the largest optimization problems in industry. Every week we decide where millions of products should sit across hundreds of fulfillment centers, how inventory should flow between suppliers, buildings, and customers, and how to balance cost, speed, and capacity, all under deeply uncertain demand. No single model can solve a problem this large. The Supply Chain Planning Optimization team is building the next generation of planning systems around large-scale distributed optimization: decomposing the full network problem into tractable pieces that coordinate toward a globally consistent plan, solving optimization problems with hundreds of thousands of variables in seconds, and pairing them with probabilistic forecasts so plans hold up when reality diverges from the forecast. The work spans the full stack of modern operations research, from decomposition and convergence to stochastic optimization and solver performance at scale. And it has a rare property: the models you build move real inventory for hundreds of millions of customers, and you see the results in the physical world within weeks. What you'll do Design and deploy large-scale optimization and forecasting models that plan inventory placement and flow across our EU/NA fulfillment network under uncertainty Shape how the full network problem is decomposed and coordinated, defining the mathematical architecture of the planning system rather than just the models within it Push the computational frontier through formulations that solve fast and reliably at scale, and through the solver technology and tooling that make experimentation cheap Work with science, engineering, operations, and finance partners to take ideas from whiteboard to production, then own them end to end once live What we're looking for An experienced scientist with depth in large-scale optimization (stochastic optimization and decomposition methods especially welcome), fluency in machine learning and probabilistic forecasting, and a track record of delivering complex scientific systems end to end. You care about both the elegance of a formulation and whether it solves in two seconds or two hundred, and you're energized by delivering incremental wins while building toward a long-term scientific vision. If you want your optimization theory to move real inventory at planetary scale, this is the team. Key job responsibilities Build state-of-the-art, robust, and scalable stochastic optimization and probabilistic forecasting algorithms that drive optimal planning and execution under uncertainty across Amazon's end-to-end supply chain Shape how large-scale planning problems are formulated, decomposed, and solved — designing for computational performance and reliability at the scale of Amazon's fulfillment network Engineer your algorithms as production-grade, cloud-native software, applying modern development practices from prototype through deployment Think several steps ahead: architect long-term scientific solutions while continuously shipping incremental improvements to what's already running Prototype fast, drive early adoption through pilots, integrate operational feedback, and iterate Deliver your science into production by partnering closely with internal customers — understanding their needs and blockers, and influencing their roadmaps Lead complex analyses and communicate results and recommendations crisply to senior leadership Stay at the frontier as an active member of the science community: research, apply, and publish (internally and externally) the latest OR/ML techniques from academia and industry About the team We are a team of scientists and engineers who believe that some of the hardest optimization problems in the world are hiding inside everyday questions like "where should this product sit so a customer gets it tomorrow?" We take ideas from the frontier of operations research and machine learning — distributed optimization, planning under uncertainty, solving at massive scale — and turn them into systems that steer one of the largest supply chains on Earth. If a model we ship on Monday moves millions of units by Friday, that's a normal week; that loop between theory and the physical world is why we're here.