Careers

Do you want to build the ML models that power every compliance verdict customers and auditors rely on? We are building a new Governance, Risk, and Compliance (GRC) service at AWS where every action — human or agent — is recorded, assessed, and provably governed, continuously and at any scale. As an Applied Scientist on this team, you will own the Natural Language Inference (NLI) model at the center of our compliance engine: fine-tuning, calibrating, and monitoring it in production. You will also design the science behind framework decomposition, evidence auto-linking, and a new agent-context system that tackles retrieval, grounding, and hallucination measurement for AI agents. Your work will directly shape the accuracy and trustworthiness of every customer-visible result we produce. Key job responsibilities - Own the production NLI model end-to-end: fine-tune, promote, calibrate thresholds, and monitor for drift to ensure accurate compliance verdicts across frameworks. - Design and implement framework decomposition pipelines that translate compliance controls into NLI hypotheses, including enrichment and per-framework threshold calibration. - Build and improve the evidence auto-linking system using ranking, embedding, and reranking techniques, tuning precision-recall tradeoffs and addressing OCR quality challenges. - Drive the quality flywheel by establishing labeling standards, designing offline and online evaluation methods, orchestrating retraining cycles, and ensuring privacy-safe data handling. - Research and develop the agent-context capability — retrieval and representation of lifecycle signals, cross-framework knowledge, memory, grounding, and hallucination measurement — collaborating with engineering pods to bring these models into production. A day in the life You will split your time between collaborative and heads-down work. On the collaborative side, you will partner with several pods of software development engineers in an Agile cadence — aligning on data collection needs, sharing experiment results, and integrating models into production services. On the heads-down side, you will dig into model training runs, analyze evaluation metrics, and iterate on threshold tuning until the numbers meet the bar. Your work is roadmap-driven, giving you the space to plan experiments methodically while still responding to new framework onboarding requests as they arise. About the team Our team builds the AI and ML systems behind AWS Governance, Risk, & Compliance service, where science is not auxiliary — it is the core. Every customer-visible compliance result flows through models our scientists create and maintain. We are investing in next-generation capabilities including observability and governance for AI agents, and the multi-quarter Agent Context initiative is an organization-defining program that will need technical leaders. Growth here is tangible. You will own the full model lifecycle from training through production monitoring, develop rare expertise at the intersection of ML and compliance, and have a clear path to expand your scope into technical leadership as the service scales.

Amazon Prime is looking for an ambitious Economist to help create econometric insights for world-wide Prime. Prime is Amazon's premiere membership program, with over 200M members world-wide. This role is at the center of many major company decisions that impact Amazon's customers. These decisions span a variety of industries, each reflecting the diversity of Prime benefits. These range from fast-free e-commerce shipping, digital content (e.g., exclusive streaming video, music, gaming, photos), reading, healthcare, and grocery offerings. Prime Science creates insights that power these decisions. As an economist in this role, you will create statistical tools that embed causal interpretations. You will utilize massive data, state-of-the-art scientific computing, econometrics (causal, counterfactual/structural, experimentation), and machine-learning, to do so. Some of the science you create will be publishable in internal or external scientific journals and conferences. You will work closely with a team of economists, applied scientists, data professionals (business analysts, business intelligence engineers), product managers, and software/data engineers. You will create insights from descriptive statistics, as well as from novel statistical and econometric models. You will create internal-to-Amazon-facing automated scientific data products to power company decisions. You will write strategic documents explaining how senior company leaders should utilize these insights to create sustainable value for customers. These leaders will often include the senior-most leaders at Amazon. The team is unique in its exposure to company-wide strategies as well as senior leadership. It operates at the research frontier of utilizing data, econometrics, artificial intelligence, and machine-learning to form business strategies. A successful candidate will have demonstrated a capacity for building, estimating, and defending statistical models (e.g., causal, counterfactual, machine-learning) using software such as R, Python, or STATA. They will have a willingness to learn and apply a broad set of statistical and computational techniques to supplement deep training in one area of econometrics. For example, many applications on the team motivate the use of structural econometrics and machine-learning. They rely on building scalable production software, which involves a broad set of world-class software-building skills often learned on-the-job. As a consequence, already-obtained knowledge of SQL, machine learning, and large-scale scientific computing using distributed computing infrastructures such as Spark-Scala or PySpark would be a plus. Additionally, this candidate will show a track-record of delivering projects well and on-time, preferably in collaboration with other team members (e.g. co-authors). Candidates must have very strong writing and emotional intelligence skills (for collaborative teamwork, often with colleagues in different functional roles), a growth mindset, and a capacity for dealing with a high-level of ambiguity. Endowed with these traits and on-the-job-growth, the role will provide the opportunity to have a large strategic, world-wide impact on the customer experiences of Prime members. About the team The Prime Economics organization is a cross-functional group of scientists (economists, research scientists, applied scientists), business intelligence engineers, software engineers and product managers embedded within Prime.

The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for training models in our RL Gym from end-to-end - Invent models for joint budget allocation, incrementality, and long-term value - Take innovations from prototype to production serving live advertiser campaigns - Raise the technical bar across scientists and engineers Key job responsibilities - Define the long-term scientific vision for our RL Gym based training process focusing on campaign optimization and other advertising workflows - Translating ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.

Are you passionate about transforming complex technical challenges into elegant, automated solutions that empower customers to leverage innovative technology? Do you thrive on building highly scalable systems that process massive amounts of data and deliver actionable insights through advanced analytics, machine learning, and AI assisted learning? Our mission: Every action - human or agent is recorded, assessed, and provably governed. Continuously and anywhere. Automatically. At any scale. We are looking for a strong Applied Scientist for new features and services that will allow our customers to fine-tune Natural Language Inference (NLI) model, framework decomposition, and evidence auto-linking that produce every customer-visible compliance verdict. About the team Our team is leading AI/ML research and development in the security and governance space. We use state-of-the-art science and engineering practices to provide continuous compliance and risk analysis, and we move fast to implement solutions and refine them based on customer feedback. Our team is adding additional focus on next-generation capabilities including AI-assisted development workflows, observability/governance for AI agents, and advanced analytics. This role will allow you to drive executive alignment and technical innovation.

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.

Have you ever placed an order on Amazon and wondered how it got to you so fast? Behind that speed is one of the most complex transportation networks in the world, and the scientific problems it poses are hard, ambiguous, and largely unsolved. We are looking for a senior research scientist who wants to invent the models that run it. Come join the Network Engineering, Scheduling and Technology (NEST) Science team within Amazon Transportation Services. We are looking for a Sr. Research Scientist to lead the design and development of novel mathematical models that govern how Amazon's Middle Mile network operates. You will work on scientifically complex, poorly structured problems in network planning, scheduling, and transportation operations, inventing and extending techniques from operations research, machine learning, stochastic modeling, and simulation to solve them. This is a modeling-first role: your primary output is rigorous, original scientific work that reduces transportation cost and improves customer experience at scale. The ideal candidate has deep expertise in at least one relevant scientific discipline and a track record of taking ambiguous business problems from formulation through to production-grade models. You are comfortable owning the scientific agenda for large problem areas, framing the right questions, inventing solutions where no textbook answer exists, and influencing scientists, engineers, and leaders across multiple teams. Key job responsibilities **Invent** - Invent and design new models and algorithms for scientifically complex problems in transportation network planning, scheduling, equipment selection, and labor optimization - Frame ambiguous, poorly defined business problems into tractable scientific formulations, distinguishing problems that require novel research from those addressable with existing techniques - Extend the state of the art in operations research, optimization, machine learning, and stochastic/discrete-event simulation as the problem demands - Author internal and external peer-reviewed publications where appropriate and not precluded by business considerations **Implement** - Lead the design, implementation, and delivery of production-grade models for complex, system-level problems, working from concept through deployment with engineering partners - Develop code (Python, Java, C++, Julia, or similar) to build, validate, and productionize models at Amazon Code Bar quality where models ship to production - Build models that are maintainable, extensible, accurate, and cost-effective, making principled trade-offs between scientific rigor and speed of execution - Design and run experiments and simulations to validate assumptions, quantify benefits, and gate rollouts on solution quality **Influence** - Drive the scientific agenda for medium-to-large problem areas, proposing new initiatives and securing leadership buy-in - Influence science and engineering strategy across multiple teams, building consensus and resolving contentious technical trade-offs - Mentor and develop other scientists, raise the team's scientific bar, and participate actively in the hiring process - Communicate complex scientific findings and recommendations clearly to technical and non-technical audiences, including senior leadership About the team The Network Engineering, Scheduling, and Technology (NEST) Science Team prototypes, builds, and productionizes mathematical models that reduce transportation cost and improve customer experience in Amazon's Middle Mile network. Equipped with techniques from Operations Research, Machine Learning, and Simulation, these models govern scheduling and equipment selection of hundreds of thousands of truck movements, optimize network configurations, determine transit times between nodes, and simulate network flow under uncertainty for informed decision making. Our core team consists of Applied, Data, and Research Scientists along with technical Product Managers from diverse backgrounds.

We are seeking a Senior Applied Scientist to focus on Robotics Spatial Intelligence and Semantic Understanding. In this role, you'll research and build advanced semantic and world understanding algorithms that enable robots to observe, understand, and reason about complex and dynamic home environments. You'll work across a broad spectrum of 3D perception, contextual understanding, and world modeling approaches to build robust solutions that support autonomous decision making, task planning, navigation, and manipulation. Key job responsibilities - Develop and implement robust World Understanding and Modeling algorithms for a domestic robot. - Build simulation-based and on-robot evaluation frameworks with comprehensive benchmarks and metrics for systematic evaluation of Our Spatial Intelligence stack. - Conduct sim-to-real transfer experiments, analyzing performance gaps and developing techniques to ensure reliable real-world performance. - Collaborate with navigation, manipulation, and other teams to ensure seamless integration of World Understanding capabilities. - Stay current with the latest advances in World Modeling, Spatial Reasoning, and related fields and apply relevant findings to improve system performance About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

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.