Careers

Reinventing How the World Shops! We are building the future of human-AI collaboration in commerce. We are creating an AI-native shopping partner that truly understands what customers mean, what they need, and what they haven't yet realized they want—rivaling the intuition of the best human experts, operating at a scale no human ever could. This is a complex personalization challenge. We sit at the intersection of massive-scale language understanding, real-time decision systems, hundreds of millions of customers, and billions of products. Our mission is to collapse the distance between intent and discovery—to make the leap from "searching for products" to "being understood as a person." As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, multidisciplinary organization, and drive breakthroughs that reshape how Amazon thinks about customers and products at the deepest level. The problems you'll solve don't have textbook answers. You will pioneer next-generation LLM-based reasoning systems that build rich, evolving models of customer intent. You will design transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. You will invent real-time, multi-objective ranking systems that balance exploration, personalization, and serendipity at billions of decisions per day. And you will do all of this as a force multiplier, building foundational technology that empowers teams across Amazon to deliver experiences that feel almost magical. Your work will be felt, not just measured. Every model you build ships directly to hundreds of millions of customers. The feedback loop between your science and real human delight is immediate. This role offers a rare combination of intellectual depth, technical ambition, and tangible impact. Come define what shopping looks like in the age of AI! Key job responsibilities - Innovate new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey. - Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime - Be a key leader on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers - Drive the science roadmap across multiple teams, helping coordinate a cohesive science agenda across the org. - Mentoring applied scientists across the org, growing their skills and careers. About the team Our mission is to delight every Amazon customer with a personalized shopping experience tailed to their intent. We achieve our mission through investments in Science, UX, and central systems with the purpose of delivering the future of shopping on Amazon. We are seeking a Principal Applied Scientist to lead the science charter across the recommendations and intent identification space.

We are Amazon's central Responsible AI team. Our mission is to advance the science and practice of Responsible AI (RAI) to enable Amazon's tens of thousands of builders to build and deploy AI solutions to the high standards that our customers and society expect. As a scientist on this team, you will: - understand in depth the technical and scientific issues related to RAI, including controllability, security, privacy, safety, veracity, robustness, fairness, explainability, transparency, and governance - help define the strategies, priorities and metrics for RAI - solve open problems in RAI to unblock traditional, generative and/or agentic AI use cases and solutions, publishing as appropriate - help develop our team - liase with internal and external stakeholders, including the academic community, on issues related to RAI About the team Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the 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. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, and other career-advancing resources here to help you develop into a better-rounded professional. 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Amazon Prime is building the future of AI-based personalization in driving long-term value out of the Prime subscription. Join our team of Scientists and Engineers developing AI/ML models to predict the Prime customer long-term behavior and optimize the customer experience with the Prime program. This includes identifying who our customers are, modeling customer behavior, and creating personalization systems to optimize the experience. As an AI/ML expert, you will partner directly with product owners to define the vision for this space and drive the roadmap for team execution. This is a complex personalization and optimization challenge. We sit at the intersection of massive-scale subscription and behavioral data, real-time decision systems, hundreds of millions of customers, and intersection with other shopping and benefit data across Amazon. Our vision is to enable a data-driven intelligent foundation that powers the future of Prime through scalable personalization, generation, and decisioning capabilities that serve optimal, cohesive customer experiences across all touchpoints. We build the science and infrastructure that allows Prime to know each member deeply, decide what to show them and when, generate the right content in the right format, and measure whether it worked — all optimized toward long-term member value. As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, cross-cutting organization, and drive breakthroughs that reshape how Prime thinks about customers and the membership program at the deepest level. You will pioneer next-generation Gen AI / LLM-based foundation models that build rich, evolving models of customer intent. This will include building Gen AI/transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. It will require thinking about real-time, multi-objective, globally scalable ranking systems that address cold start problems at billions of decisions per day. You will do all of this as a force multiplier, building foundational technology that empowers scientists and engineers, and driving teams across Prime and Amazon to deliver the best science outcomes for customers. Come define what the Prime membership program and experience looks like in the age of AI!

Reinventing How the World Shops! We are building the future of human-AI collaboration in commerce. We are creating an AI-native shopping partner that truly understands what customers mean, what they need, and what they haven't yet realized they want—rivaling the intuition of the best human experts, operating at a scale no human ever could. This is a complex personalization challenge. We sit at the intersection of massive-scale language understanding, real-time decision systems, hundreds of millions of customers, and billions of products. Our mission is to collapse the distance between intent and discovery—to make the leap from "searching for products" to "being understood as a person." As a Principal Applied Scientist, you will be the intellectual engine behind this transformation. You will define the frontier, architect the science strategy for a large, multidisciplinary organization, and drive breakthroughs that reshape how Amazon thinks about customers and products at the deepest level. The problems you'll solve don't have textbook answers. You will pioneer next-generation LLM-based reasoning systems that build rich, evolving models of customer intent. You will design transformer architectures that abstract noisy behavioral signals into high-quality latent representations of human preference. You will invent real-time, multi-objective ranking systems that balance exploration, personalization, and serendipity at billions of decisions per day. And you will do all of this as a force multiplier, building foundational technology that empowers teams across Amazon to deliver experiences that feel almost magical. Your work will be felt, not just measured. Every model you build ships directly to hundreds of millions of customers. The feedback loop between your science and real human delight is immediate. This role offers a rare combination of intellectual depth, technical ambition, and tangible impact. Come define what shopping looks like in the age of AI! Key job responsibilities - Innovate new features and models that have huge impact on the customer experience. Help customers find the right products and content on their shopping journey. - Leverage the use of advanced machine learning to create customer shopping experience at Amazon's scale - for all Amazon customers across all countries in realtime - Be a key leader on a multidisciplinary team across science, product, design, and engineering to see through ideas from inception, prototype, to launch in the hands of all Amazon's customers - Drive the science roadmap across multiple teams, helping coordinate a cohesive science agenda across the org. - Mentoring applied scientists across the org, growing their skills and careers. About the team Our mission is to delight every Amazon customer with a personalized shopping experience tailed to their intent. We achieve our mission through investments in Science, UX, and central systems with the purpose of delivering the future of shopping on Amazon. We are seeking a Principal Applied Scientist to lead the science charter across the recommendations and intent identification space.

We are seeking an exceptional Applied Scientist, Global Selling Partner Risk Intelligence and Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon's store. This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to identify compromised accounts, fraudulent ownership transfers, identity manipulation, and coordinated bad actor networks across the entire seller lifecycle, from registration through ongoing account management. You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle. You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and account relationship graphs to build predictive systems that stay ahead of evolving adversarial tactics. Key job responsibilities * Design and build predictive risk detection models using advanced AI techniques, including graph-based and network analysis methods, to proactively identify bad actors and prevent marketplace abuse at scale * Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels * Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience * Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas * Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness About the team GRIP Science (Global Risk Intelligence & Prevention) is the core detection engine within TSI Science. We build and operate ML models that identify and block bad sellers across Amazon's 24 global marketplaces. Our models cover the full seller journey, from the moment they register through every action they take on the marketplace, and we score holistic risk across all active sellers.

We are looking for a talented Applied Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores. Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions Own the model development lifecycle from experimentation through deployment — collaborating with software and ML engineers to ensure models meet latency, throughput, and reliability requirements at production scale Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team A day in the life As an Applied Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, collaborating with engineers to deploy computer vision models to production, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency

Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Senior Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities - Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. - Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. - As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. - Guide technical direction for specific research initiatives, ensuring robust performance in production environments. - Mentor fellow scientists while maintaining strong individual technical contributions. A day in the life As a member of the Delivery Foundation Model team, you’ll spend your day on the following: - Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure - Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning - Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems- Drive technical discussions within the team and and key stakeholders - Conduct experiments and prototype new ideas - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

We are seeking an exceptional Applied Scientist to join our JP Seller Services team, where you will reimagine how science analysis and modeling are conducted across the organization through an AI-native approach. In this role, you will design and build intelligent systems that enable any team member to validate business hypotheses with scientific rigor in hours rather than months. You will architect production-grade platforms spanning multi-agent AI frameworks, causal inference automation, generative AI, and simulation engines that democratize advanced analytics at scale. Your work will fundamentally transform how the teams generate, test, and deploy data-driven recommendations, scaling rigorous science solutions for every decision-maker to solve customer problems. The ideal candidate combines deep expertise in scientific analysis such as causal inference, machine learning, and AI system design with the vision to rethink the entire science lifecycle from hypothesis to deployment. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. Key job responsibilities - Lead the design and development of AI-native science platforms that automate the end-to-end lifecycle from hypothesis formulation through causal analysis, model validation, and deployment into production systems. - Design and build shared knowledge infrastructure (feature stores, experiment registries, model leaderboards) that enables cumulative organizational learning, where every validated insight accelerates future analyses. - Design and implement evaluation frameworks, including Seller simulations, that enable teams to validate model quality and test interventions against synthetic populations before live deployment. - Drive integration with downstream systems to close the gap between validated insights and seller-facing actions, ensuring science outputs reach the people and systems that serve customers. - Collaborate with cross-functional partners (product managers, category leaders, marketing managers, economists, and data scientists) to identify high-impact business problems and translate them into scalable scientific solutions.

Amazon Robotics is revolutionizing warehouse automation at unprecedented scale. We are looking for a technical lead to own the whole-body planning, control, and optimization stack for the Phoenix mobile manipulation robot. In this role, you will design and ship motion planners and controllers that coordinate all of Phoenix's degrees of freedom (mobile base, torso, arm, and end-effector) as a single unified system, enabling smooth, safe, and production-ready autonomous manipulation in Amazon fulfillment environments. You will architect whole-body motion planning pipelines (task-and-motion planning, trajectory optimization) that generate collision-free, time-efficient trajectories across the full kinematic chain. You will develop and tune real-time whole-body controllers, including QP-based and model-predictive approaches, and integrate them with the Foundry Controller mainline. A key part of the role is ensuring planner-generated trajectories are high-quality enough to train downstream foundation model policies (e.g., π0.5) at scale. You will also extend Phoenix's Control Barrier Function (CBF) safety architecture, guaranteeing constraint satisfaction from planning through execution. About the team Amazon Robotics develops and deploys advanced robotic systems that power Amazon's fulfillment centers worldwide. Our innovations in mobile manipulation, autonomous navigation, and human-robot collaboration are transforming how products move through our network, enabling faster delivery times while creating safer, more ergonomic work environments for our associates.

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. Humorphic Labs builds AI systems that work as teammates rather than tools. We ship them six months or more ahead of anyone else. The Lab is small on purpose so it can change direction in a day. Humorphism concerns the quality of the working relationship between a person and an AI system. It asks whether the system acts proactively adapts to the person and the situation earns trust manages attention and strengthens human judgment. This role turns those behaviors into testable questions then answers them with working systems. You will own the scientific agenda. You will convert a fuzzy behavioral goal into an end-to-end plan that covers data agent architecture evaluation and the product experiment that tests it. You will write code every week and build large parts of the experimental stack yourself because it does not exist yet. The first focus areas are agentic products where a domain expert holds judgment the system cannot replace. Amazon Connect places an assistant beside a person handling a live conversation. AWS Bio Discovery places one beside a scientist running experiments. Both need evaluation that measures collaboration quality rather than task completion alone. The role changes shape as the Lab matures. It starts as hands-on science in close partnership with product and engineering. Later it moves inside a product team to carry adoption of what the Lab proved. Key job responsibilities • Own the scientific strategy for human-AI collaboration across agentic and multimodal systems. • Convert desired interaction behaviors into falsifiable hypotheses evaluation tasks and system requirements. • Build the evaluation system that measures teammate behavior trust adaptation human contribution and failure recovery. • Design agent systems that use memory tools planning and recovery then test them with the people who do the work. • Design data collection and curation for language speech and interaction traces. • Build significant parts of the experimental stack yourself because that stack does not exist yet. • Diagnose failures across data models orchestration evaluation and product interaction. • Define the requirements engineering needs to turn a proven method into a product capability. • Partner with design product engineering and behavioral research from problem definition through product validation. • Run experiments with partner product teams then report what worked what failed and what changed as a result. • Set the standard for reproducible experiments evidence and scientific review inside the Lab. • Mentor scientists and engineers without moving away from hands-on work. • Represent the work in internal reviews and in appropriate external scientific venues. A day in the life Your week has two centers of gravity. Most days you build. You take a claim about how a teammate should behave design the smallest experiment that can falsify it and run it. You read interaction traces from real sessions then argue with the engineers about what they mean. The rest of the week belongs to the people the work is for. You sit with a contact center agent or a bench scientist and watch where the system helps and where it intrudes. You leave with the next hypothesis. Often you leave with evidence that kills the last one. About the team 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 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. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. 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 and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.