Careers

Core to Amazon's DNA is its methodology around executing experiments and prototypes to find the quickest way to prove new ideas, technologies, and business models in a real-world environment. Experimentation fuels Amazon's innovation. Our team does horizon exploration. We take emerging technology, including our own inventions, build working prototypes with customers and partners, and demonstrate what becomes possible before the rest of the market gets there. Prototyping is how we do the work. Pushing the technology horizon is why we do it. If you are a highly creative problem solver who works to inspire, motivate, and define innovative solutions; are passionate about technology, understand cloud architectures and platforms, and are quick to pick up emerging technologies; are adept at working with customers to experiment with innovative approaches and validate the technical feasibility of solutions, this could be the role for you. As a Senior Applied Scientist you sit directly with customers and partners. You help them envision complex uses of AWS by building working prototypes of specific use cases, tailored to their data, devices, and systems. Engagements run in short build cycles, typically four to six weeks, and are designed to be innovative and evaluative, demonstrating the art of the possible on AWS while proving a credible path to production. You will work across the areas where we are placing our biggest bets. These shift as the technology does, but today they include: - Physical AI and autonomous robotics. World models, vision-language-action (VLA) models, cloud-based and agentic VLA, real-to-sim and sim-to-real pipelines, and agentic control of physical systems. We challenge the assumption that these models have to live only at the edge. - Autonomous scientific discovery. Self-driving labs and instrument intelligence, where agents plan, run, and interpret experiments across fleets of scientific instruments. - Agentic developer intelligence. Multi-agent systems, code agents, and automated research workflows that change how software gets built. - Scaling what we learn. Turning one-off prototypes into reusable patterns and assets the broader field can pick up and run with. You will not just apply these techniques. You will help decide which bets are worth making and prove or disprove them in the open with real customer problems. Key job responsibilities - As a key member of the AWS Specialists and Partner Prototyping team, ensure customer success through hands-on participation producing and demonstrating prototypes on the AWS platform. - Participate in deep architectural and scientific design discussions to ensure prototypes are crafted for successful deployment in the cloud and a credible path to production. - Bring applied science depth to engagements: frame the problem, select and adapt models, design experiments, and judge technical feasibility honestly, including when the answer is that an approach will not work. - Conduct one-to-few and one-to-many training sessions to transfer knowledge to prototyping customers, partners, and internal business partners. - Act as a thought leader for your area of domain expertise across AWS, and share best-practice knowledge among the community. - Author or otherwise contribute to AWS customer-facing publications such as peer reviewed position papers, blogs, and reference patterns. - Act as a technical liaison between customers, service engineering teams, and sales, carrying real-world signal back to the teams building the services. A day in the life No two engagements look the same, which is the point. A typical week might include: - Running a working session with a customer's scientists and engineers to pressure-test an idea and agree on what a prototype needs to prove. - Writing code and training or fine-tuning models in the lab, often against the customer's own data, devices, or robots. - Pairing with a prototyping SA and engagement manager, the standard pod you deliver alongside, to keep the build moving and the scope honest. - Demonstrating a working prototype live and walking stakeholders through what it does, what it does not yet do, and what production would take. - Capturing what you learned as a reusable pattern or publication so the next team does not start from zero. - Spending time on the horizon itself: reading, experimenting with a new model or technique, and forming a point of view on where a bet is heading. You will move between hands-on building and customer-facing explanation constantly. The best people in this role are equally comfortable in a research paper, a code editor, and a room full of executives. About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) conferences, inspire us to never stop embracing our uniqueness. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Amazon Advertising operates at a scale few places in the world can match, and every ad shown to a customer must meet a high bar for trust and quality. We are looking for an Applied Scientist II to advance the machine learning systems that evaluate advertising content at scale — balancing accuracy, latency, and cost so that we protect the customer experience without slowing advertisers down. You will own meaningful scientific problems end to end: framing the problem, building and validating models, and taking them to production where they make decisions on live traffic. This is a hands-on science role with real ownership. You will work across large multilingual datasets, modern ML and large language model techniques, and tight engineering constraints, partnering closely with other scientists, engineers, and product teams to turn research into measurable customer impact. Key job responsibilities - Design, build, and ship ML models and systems that classify and evaluate content accurately and efficiently at scale. - Translate ambiguous business and policy problems into well-scoped scientific problems with clear success metrics. - Apply and adapt modern techniques — including deep learning, representation learning, and large/small language models — to production problems under real latency and cost constraints. - Own the full model lifecycle: data, training, evaluation, deployment, monitoring, and retraining in response to drift and changing requirements. - Define evaluation methodology and the metrics that matter (precision, recall, cost of error, latency), and defend trade-offs with evidence. - Collaborate with engineering and product partners to integrate models into live systems, and mentor junior scientists.

Lead the development, validation, and governance of the full suite of risk models for a high-growth payment product in Mexico, directly shaping the analytical foundation that drives every business decision across the product lifecycle. If you thrive on building scorecards from the ground up — starting with external data and expert-rule frameworks when proprietary data does not yet exist, then architecting the transition to custom models as portfolio mature — this is your opportunity to establish and scale a risk modeling function from inception. As Sr. Data Scientist, you will own the end-to-end model roadmap spanning origination, behavioral, delinquency propensity, collections, recovery, analytical fraud, and reserve input models. You will define development standards, documentation practices, versioning protocols, and monitoring frameworks with the rigor required to withstand audit scrutiny and regulatory review. Your work is both tactical and strategic: you will manage complex, cross-functional model programs with negotiated and defined obligations, influencing roadmaps, business and technology decisions, and model governance across the organization. Success in this role is measured by discriminatory power (KS, Gini) and its stability over time, Population Stability Index (PSI) of variables and scores, development-to-implementation cycle time, and the absence of validation findings. In a new product, the first 12 to 18 months are typically operated with generic or bureau scorecards and expert rules; this role requires someone who knows how to launch without proprietary data and plan the deliberate transition to custom models built on proprietary performance data as vintages mature. Key job responsibilities You will own and execute the model roadmap covering origination scorecards, behavioral scorecards, delinquency propensity models, collections and recovery models, analytical fraud models, and reserve inputs (PD, LGD, EAD). You will manage the full model lifecycle: development, validation, approval, implementation, ongoing monitoring, recalibration, and retirement. You will develop the origination scorecard, including reject inference methodology, and lead feature engineering across external data sources, and partner transactional data. You will build behavioral scorecards,payment propensity, contactability, and recovery models and produce PD, LGD, and EAD inputs for reserves calculation. You will own model validation end-to-end: conducting conceptual soundness reviews, replicating results, performing stress testing and stability analysis, and issuing formal opinions with findings, severity classifications, and remediation timelines. You will produce complete technical documentation and model files to the standard required for third-party review. A day in the life You will join working sessions with the risk team to translate champion/challenger experiment results into model refinement priorities, and calibrate payment propensity and contactability models against real-world recovery outcomes. You will coordinate with engineering to validate that deployed model versions match approved specifications and prepare the model performance package for the credit and risk committees. About the team We are a team at Amazon building a new business, and we sit within the Payments organization. Today the team is small by design, which means you will have an outsized role in shaping our credit risk function, processes, and culture from day one. Our closest partners span finance, technology, and commercial teams, and together we are investing in creating a world-class underwriting and credit risk capability. If you want to help define something new at Amazon and see your work directly reflected in the product, we would love to hear from you.

Are you excited about using econometrics and statistics to impact real-world business decisions? We are looking for an Economist to build innovative solutions based on econometrics, experimentation, and machine learning helping us to answer challenging and impactful business problems for Prime Video. If you have an entrepreneurial spirit, you know how to deliver results fast, and you have a deeply quantitative, highly innovative approach to solving problems, and long for the opportunity to build pioneering solutions to challenging problems, we want to talk to you. Key job responsibilities - Leverage causal inference methods to estimate the impact of large-scale investments - Develop advanced structural demand and econometric frameworks for products and services - Collaborate with other scientists at Amazon to deliver measurable progress and change - Influence business leaders based on empirical findings A day in the life You will work as an individual contributor working on a mix of complex strategic, long-term research as well as tactical analytical projects. You will write technical as well as business-facing docs and present your work to business leaders and the science community. About the team The Prime Video Hub Science team combines data science and economics to measure how Prime Video initiatives drive customer engagement and business value. We design experiments across Prime Video and Amazon surfaces, build causal models to evaluate marketing and product initiatives across channels, and translate findings into actionable metrics and recommendations.

If you are an economist who dreams of shaping an Amazon-wide measurement program and possess the confidence to navigate early-stage ambiguity, read on. Amazon's customers rely on the completeness, consistency, and correctness of Amazon's product data to make well-informed purchase decisions. Improving the quality of that product data is a continuous effort spanning hundreds of initiatives across the company — and understanding how much those improvements actually move the needle for customers and the business is one of the hardest and most valuable problems in the space. Amazon Selection Catalog Systems (ASCS) is looking for a Senior Economist to join the Catalog Experimentation and Impact Measurement team. Our mission is to be the source of truth for the causal impact of catalog quality improvements across Amazon. We answer a deceptively simple question at massive scale: when we make product data better, how much do customers benefit and how much incremental business does it drive? We do this through two complementary approaches. First, we design and run controlled experiments that establish rigorous, causal links between improvements in product data quality and customer and business outcomes — turning what used to be slow, lagging estimates into fast, decomposable, decision-ready measurement. Second, we operate a federated measurement model: partner teams across Amazon run their own experiments and analyses for their launches, and we set the scientific standard — advising on experimental design, reviewing methodology, and aggregating results into a single, trusted view of total impact for leadership. We are looking for an economist with demonstrated experience conducting causal inference at scale. Our problems center on attributing value to actions in the complex world of catalog information and customer behavior — isolating causal effects amid noise, selection, and interference, and doing so credibly enough to guide decisions at the highest levels. The ideal candidate combines deep acumen in econometrics, experimental design, and data science to grapple with these challenges and to raise the measurement bar for the entire organization. This is an opportunity to influence catalog quality improvements across Amazon and to build a measurement program from the ground up — one with the rigor, transparency, and scale to become the standard the whole company trusts. You will not only measure impact; you will define how Amazon measures it. Key job responsibilities Design and run experiments at scale. Conceive, design, and execute controlled experiments that establish rigorous causal links between improvements in product data quality and customer and business outcomes, producing fast, decomposable, decision-ready measurement. Develop causal models and methodology. Build and refine econometric and causal-inference methods to attribute incremental impact to catalog actions amid noise, selection, and interference — and advance the state of the art in how impact is measured across the organization. Set the scientific standard for a federated measurement model. Advise partner teams across Amazon on experimental design and analysis, review their methodology, and ensure their results meet a consistent, defensible scientific bar. Aggregate and report total impact. Synthesize results from both direct experiments and partner-run analyses into a single, trusted view of total impact, and communicate it clearly to senior leadership. Translate ambiguity into programs. Take open-ended, early-stage measurement problems and shape them into durable programs with clear methodology, reproducible pipelines, and transparent confidence levels. Influence decision-making at the highest levels. Partner with product, engineering, and business leaders to turn measurement insights into prioritization decisions on catalog quality investments across hundreds of initiatives. Raise the bar on rigor and transparency. Champion reproducibility, clearly-stated assumptions, and honest reporting of uncertainty (including confidence intervals) so the measurement program becomes the standard the whole company trusts. A day in the life Design an experiment. Scope a controlled experiment to quantify how a product-data improvement affects customer and business outcomes — defining the hypothesis, treatment, randomization unit, and sample size. Model and analyze. Pull large catalog and behavioral datasets, build and refine causal-inference models, and stress-test results for robustness and transparency. Advise partner teams. Review a partner team's experiment design, flag methodological risks, and help them meet the scientific bar before launch. Report impact that leaders act on. Interpret results, quantify uncertainty honestly, and fold your findings and partner analyses into the consolidated view of total impact leadership relies on. Raise the bar. Partner with product and business leaders to turn a measurement insight into a catalog-quality investment decision, and share reusable methods that level up teams across the organization.

Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies — all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows from Originals and Exclusive content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business — available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. Prime Video Commerce's mission is to present the right offer to the right customer at the right time — across subscriptions, channels, and transactional video, in every market and on every device. Our science team replaces static business rules with ML-driven decisions that personalise the entire commerce journey, from discovery through checkout and beyond. We operate at scale across hundreds of millions of customers, and we are expanding into new frontiers — combining the latest advances in agentic and generative AI, behavioural simulation, and causal inference to understand the impact of our decisions before they reach customers. We are looking for an Applied Scientist to join the Prime Video Commerce Insights team in London. You will develop and deploy customer-facing models, understand customer behaviour at scale, and explore emerging techniques that help us make better decisions faster. This is a delivery focused role within a high-visibility multidisciplinary group of engineers and scientists, focused on improving the customer experience for Prime Video. Key job responsibilities - Research, design, and implement machine learning approaches (e.g. reinforcement learning and recommendation systems) that personalise across different customer touch points. - Collaborate with engineers to deploy and integrate successful experiment results into large-scale, complex Amazon production systems with low latency. - Design and execute rigorous experiments to demonstrate the technical efficacy and business value of your methods. - Act as a subject-matter expert and help define the science roadmap and research agenda in line with organisational priorities and production constraints. - Provide machine learning thought leadership to technical and business leaders, thinking strategically about business, product, and technical challenges. - Work with technical product managers to work backwards from what matters to customers and deliver ML-backed solutions. - Share results with the team and wider scientific community through documents that are both statistically rigorous and compellingly relevant. A day in the life You will be a research leader and innovator within the Commerce Insights organisation. You will collaborate with talented engineers and senior leaders to solve problems that are uniquely challenging at Amazon's scale: personalising commerce decisions across multiple business lines, balancing competing objectives, and positively impacting hundreds of millions of customers worldwide. The problems here are technically deep — combining large-scale ML, causal reasoning, and behavioural modelling in a domain where every decision carries real revenue and customer-experience consequences. Your research will ship to production and move metrics that matter. About the team You will join a team of engineers and applied scientists with a proven track record of solving highly complex, ambiguous problems — work that has produced patents and publications at top-tier conferences. The team has direct visibility to senior Prime Video leadership and collaborates broadly across Commerce, Content, and Platform teams to shape how customers discover, subscribe to, and engage with video content. This is a team that operates at the intersection of rigorous research and real-world impact, where your ideas move from whiteboard to production for hundreds of millions of customers.

Some of the hardest problems in security are waiting on models that do not exist yet. As a Principal Applied Scientist, you will create them. You start from the strongest models in the world and push them to excel at problems no off-the-shelf model has ever seen. This is one of the largest AI infrastructure programs AWS has ever taken on, and you will train on latest-generation GPU hardware, among the first anywhere to put it to work at this scale. Much of this has no established playbook. There is no benchmark to tell you a model is ready, so you will invent both the method that gets it there and the measure that proves it. The lab is purpose-built for this work and you join at the start, helping shape its technical direction and building the methods and evaluations that carry forward into everything it does next. This is a hands-on role. You will research the state of the art science, make the calls on how models are trained and evaluated, write code and work alongside the engineers who run the infrastructure, and partner directly with the analysts and operators who depend on the work. You will also explain what the models can and cannot yet do, in plain terms, to the people whose decisions rely on them. The candidate selected must obtain and maintain a security clearance at the TS/SCI with polygraph level. Upon start, the selected candidate will be sponsored for a commensurate clearance for each government agency for which they perform AWS work. Key job responsibilities • Own the end-to-end adaptation of frontier models for complex reasoning tasks, from supervised fine-tuning and post-training through reinforcement learning, delivered as production systems people can rely on in the field • Define what "good" means for each task, building the evaluation sets, test harnesses, and metrics that measure model quality where no public benchmark exists • Build and own the data pipelines that turn raw operational data into high-signal training and evaluation sets, including curation, quality scoring, provenance tracking, and strict leakage and contamination controls • Judge when a problem needs a genuinely new method versus an extension of an existing one, and make the call with evidence • Choose the training and adaptation methods, including parameter-efficient techniques, that turn serious dedicated compute into the largest gains in capability • Extend adapted models into new priority domains, owning transferability, generalization, and calibration under real-world distribution shift • Run data-preparation and human-in-the-loop feedback loops with the analysts who know the problems best, turning their expertise into better data and sharper evaluations • Package methods and evaluations so they can be reused as the lab's work grows, and shared with Amazon's model teams • Represent the lab's science in technical reviews and to program and technical leaders, surfacing what the models can do and where the risks are • Help set the technical bar for the lab, and mentor scientists as the team grows A day in the life You start the morning reviewing an overnight fine-tuning run against an evaluation suite you built, checking whether the adapted model beats the base on the metrics that matter most. Mid-morning, you work with the engineers who run the infrastructure to get a large training run moving and squeeze the most capability out of it. After lunch, you sit with domain analysts to turn their feedback into a cleaner, better-labeled evaluation set for a change-detection task, then decide whether the next gain calls for a new method or an extension of one you already have. You close the day documenting that method and evaluation so the rest of the lab can build on it, and writing a short, plain-language readout of what the model can and cannot yet do for the people who depend on it. About the team The Mission AI Model Lab is the science team behind AWS's Mission AI program. We take existing models and make them useful for problems that matter to national security, working on dedicated AI infrastructure built for exactly that. This lab is purpose-built and these are among its first scientists, so the people who join now help shape how it works and what it builds. We sit where frontier-model research meets national security delivery, and we value invention on genuinely new problems, evaluation we can defend, and plain talk with the people who rely on what we build.

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. About this role In this role, you'll employ scalable machine learning (ML), deep learning (DL), and Natural Language Processing (NLP) techniques to detect and predict fraudulent activities, enhance fraud investigation capabilities, and develop advanced fraud protection and defense mechanisms. You'll leverage these technologies to analyze complex patterns in transaction data, identify anomalies, and create predictive models that can anticipate potential fraud before it occurs. Your work will be crucial in safeguarding the company's assets, protecting customers from financial harm, and maintaining the integrity of our systems. You'll translate intricate fraud patterns into actionable insights, enabling rapid response to emerging threats and informing critical business decisions related to risk management. You'll operate in an agile environment in which we own and collaborate on the life cycle of research, design, and model development of relevant projects. As an Applied Scientist, you will... - Protect Audible’s customers and content creators against the onslaught of AI-generated fraud - Develop Amazon-scale data engineering & modeling pipelines. - Imagine and invent before the business asks, and create novel fraud detection and mitigation solutions using innovative approaches. - Work closely with other data scientists, ML experts, engineers as well as business across the globe, and on cross-disciplinary efforts with other scientists within Amazon. - Contribute to the growth of the Audible Data Science team by sharing your ideas, intellectual property and learning from others. ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

AWS is seeking a Data Scientist to join our Consumer Experience team. We're looking for someone who is curious about large-scale data challenges and motivated to apply ML/AI and advanced analytics to solve real customer problems. Reporting to the Science Lead, this role supports the analytical capabilities of AWS by applying emerging technologies, contributing to data science best practices, and partnering with stakeholders to deepen understanding of the AWS customer experience. The ideal candidate has experience working in complex data and analytics environments, with hands-on skills in Machine Learning and advanced analytics techniques — preferably within cloud computing. You should be comfortable building models and equally comfortable explaining what they mean to a non-technical audience. Key job responsibilities - Apply statistical analysis, ML, and complex data mining techniques to deliver insights that shape the AWS customer experience. - Build production-ready analytics solutions that support effective business decisions. - Partner with product, engineering, and design teams to develop and launch data products. - Contribute to the adoption of Gen AI and ML-based approaches across the data science practice, including building proof-of-concept AI-driven customer use cases. - Communicate technical findings clearly to both technical and non-technical stakeholders. - Bring new ideas — share approaches, tools, and methods that raise the bar for the team.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.COM SERVICES LLC Offered Position: Applied Scientist III Job Location: Santa Monica, California Job Number: AMZ10564053 Position Responsibilities: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. 40 hours / week, 8:00am-5:00pm, Salary Range: $167,100/year to $226,100/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000