Careers

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle unparalleled object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to advance reinforcement learning for manipulation. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will focus on training these policies in simulation at scale and transferring them to physical robots. You will join a small team whose mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to collect data, run experiments, and get new policies onto real hardware faster than almost anywhere in the field. Key job responsibilities - Design and train reinforcement learning policies for non-prehensile and contact-rich manipulation, focused on the long tail of diverse, demanding conditions. - Build and scale simulation environments, training curricula, and reward formulations that produce policies which transfer to real robots. - Develop sim-to-real methods (domain randomization, system identification, and related techniques) and validate them on physical hardware. - Write production-quality code and own scalable, efficient training and evaluation pipelines. - Evaluate policy behavior and failure modes, and iterate between simulation and real-world testing to improve robustness. - Partner with scientists and engineers across control, perception, and hardware to move ideas from prototype to demonstrated capability. - Represent Amazon Robotics in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that assist you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments, at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an Applied Scientist to bring learned manipulation policies to life on real robots. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will focus on making learned, contact-rich, non-prehensile behaviors work reliably on physical hardware, closing the loop between what a policy does in simulation and how it behaves against real contact, sensing, and dynamics. You will join a small team whose mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to test new policies and behaviors on real hardware faster than almost anywhere in the field. Key job responsibilities - Deploy and evaluate learned manipulation policies on physical robots, focused on the long tail of diverse, demanding conditions. - Diagnose the gap between simulated and real behavior, and drive the changes in policies, rewards, sensing, or control that close it. - Develop tactile, actuation, and contact-aware methods that make non-prehensile manipulation fast, assured, and robust. - Write production-quality code and own scalable, real-time implementations that run on robots. - Build the data collection, logging, and evaluation loops that turn real-world experience into policy improvements. - Partner with scientists and engineers across control, perception, and hardware to move ideas from prototype to demonstrated capability on hardware. - Represent Amazon Robotics in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that assist you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Our organization in Amazon Robotics builds robots that perform contact-rich manipulation safely and reliably in complex, unstructured environments, at Amazon scale. Our scientists and engineers push the boundaries of robotic manipulation to handle enormous object diversity, bringing deep expertise across planning, control, perception, and machine learning. We learn from real-world data at a scale that few teams in robotics can access. We are seeking an experienced Senior Applied Scientist to help guide a small team advancing reinforcement learning for manipulation. We are creating robots that learn how to push, flip, rearrange, and dexterously insert items with unparalleled robustness, speed, and reliability. Our goal is to deploy robots that will work across Amazon's global network and can handle the full diversity of items that Amazon sells. You will set the technical direction for how we learn these behaviors, from simulation training through reliable execution on physical robots, and you will demonstrate new manipulation capabilities on real hardware at scale. This team's mission reaches beyond any single product: to invent and apply manipulation capabilities that generalize to many future robotics applications. The robots our organization already deploys at scale give you a rare proving ground to collect data, run experiments, and get new policies onto real hardware faster than almost anywhere in the field. You will raise the bar for scientific rigor and engineering quality, and mentor other scientists as the team grows. Key job responsibilities - Set the technical direction for learning non-prehensile and contact-rich manipulation policies, from testing the latest advances in the field through demonstrated capability on hardware. - Oversee the development of reinforcement learning approaches that address the long tail of diverse, demanding manipulation conditions. - Own the path from simulation training to reliable, real-time execution on physical robots, making evidence based calls on where learned approaches should replace engineered ones. - Demonstrate new manipulation capabilities on real robots at scale, and turn one-off results into repeatable methods. - Establish the standards, evaluation practices, and data-informed improvement loops that the team builds on. - Mentor scientists and engineers, and raise the bar for applied science rigor and engineering quality. - Partner across control, perception, and hardware to integrate learned behaviors into working systems. - Represent Amazon in academia through publications and scientific presentations. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply

We are looking for an AI/ML Engineer to build efficient, stable foundation models for long-horizon agentic workloads. The role combines model development with systems and hardware optimization. Qualifications Strong background in machine learning, deep learning, transformers, and foundation models. Experience with PyTorch, JAX, or similar frameworks. Experience training, fine-tuning, or serving large-scale models. Knowledge of GPU architecture, CUDA, Triton, custom kernels, or hardware-aware optimization. Familiarity with distributed training and model parallelism. Experience with AWS Trainium, Inferentia, Neuron SDK, TPUs, or other custom accelerators is preferred. PhD in machine learning, computer science, electrical engineering, or a related field is preferred. Key job responsibilities Responsibilities Design, train, fine-tune, and evaluate custom foundation models. Improve model efficiency, stability, latency, throughput, and memory usage. Build models that support planning, tool use, memory, self-correction, and multi-step task execution. Optimize training and inference using CUDA, Triton, custom GPU kernels, and tensor operations. Profile and resolve compute, memory, communication, and distributed-training bottlenecks. Work with GPUs and specialized AI accelerators, including AWS Trainium and other custom tensor hardware. Develop evaluations for model reliability and long-horizon agent performance. About the team Diverse Experiences AWS 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 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. 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) and AmazeCon) conferences, inspire us to never stop embracing our uniqueness. Mentorship & 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. 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.

We are looking for an Applied Scientist to join the Robotics Simulation team at Amazon Robotics. In this role you will design, build, and validate the simulation environments and policy training pipelines that enable robots to learn manipulation and mobility skills in simulation and transfer them to real hardware. You will work at the intersection of robotics simulation science and modern Physical AI: building GPU-accelerated RL environments, implementing imitation learning workflows, characterizing sim-to-real gaps, tuning physics parameters against real-world data, and evaluating learned policies both in simulation and on physical robots. You will collaborate closely with SDEs who build platform infrastructure, Technical Artists who create simulation assets, and partner science teams who consume your environments and pipelines for their model development. This is a hands-on, execution-focused role. You will own specific simulation science deliverables end-to-end, from environment design through policy evaluation, with increasing scope and independence over time. You will contribute to technical design discussions, propose improvements to the team's simulation fidelity and training methodology, and help establish best practices for robot learning in simulation. Key job responsibilities * Design and implement GPU-accelerated reinforcement learning and imitation learning environments in NVIDIA Isaac Lab for manipulation and mobility tasks. * Build and maintain policy training pipelines supporting diverse model architectures (diffusion policies, VLAs, behavior cloning, actor-critic RL) and evaluate trained policies in simulation. * Characterize and reduce sim-to-real gaps through systematic validation: compare simulated sensor outputs, kinematics, and dynamics against real-world robot data, then implement targeted improvements. * Implement domain randomization strategies (visual, physics, geometric) to improve policy robustness and transfer to real hardware. * Develop sim-to-real transfer techniques including system identification, physics parameter calibration, and visual domain adaptation. * Create robot embodiment validation tests (joint kinematics, actuator response, contact behavior) to ensure digital twins are faithful to real hardware. * Build data pipelines for recording, replaying, and augmenting demonstration data (from teleoperation or automated trajectory generation) to scale training data volume. * Contribute to end-effector modeling and contact dynamics tuning, ensuring physically plausible gripper and tool interactions in simulation. * Author design documents for new simulation science capabilities and contribute to technical reviews. * Collaborate with partner science teams to understand their model architectures and ensure simulation environments meet their training requirements. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team The Robotics Simulation team is a multidisciplinary organization of SDEs, Applied Scientists, and Technical Artists at Amazon Robotics. We build the simulation infrastructure that powers Physical AI development, from photorealistic synthetic data to GPU-accelerated training environments. Our simulation stack enables robots to be designed, trained, and validated entirely in simulation before physical hardware exists, compressing development timelines and de-risking robotics programs across Amazon. The team delivers end-to-end simulation stacks for Amazon's robotics programs, including high-fidelity robot digital twins, teleoperation data collection infrastructure, scalable synthetic demonstration generation, policy training and inference pipelines (RL, imitation learning, VLAs), domain randomization for sim-to-real transfer, and model validation in simulation. We partner closely with hardware teams, science organizations, and robotics program leads across Amazon Robotics.

Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? If so, we have an exciting opportunity for you! Translation Services is seeking an Applied Science Manager to own the technical vision and multi-year science roadmap spanning machine translation, multimodal content (image translation, video subtitling), and automated quality evaluation. This leader will manage scientists and MLEs, define research direction for novel problem spaces with limited industry precedent, and bridge science breakthroughs into production-ready systems operating at Amazon scale. As a leader of the Science team of TS, this person will be responsible for leading their team in designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The goal is to build solutions with minimal human touch involved in any language translation and ensure accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. This role requires strong technical skills, a deep understanding of machine learning approaches, and a solid grasp on NLP and LLM techniques to solve complex language translation challenges. You must have a demonstrated ability for optimizing, developing, launching, and maintaining large-scale production systems. As a key member of the team, you will oversee all aspects of the software lifecycle: design, experimentation, implementation, and testing. You should be willing to dive deep when needed, move rapidly with a bias for action, and get things done. You should have an entrepreneurial spirit, know how to deliver, and long for the opportunity to build pioneering solutions to challenging problems. This role will demand resourcefulness and willingness to learn on both the technical and business side. Key job responsibilities In this role, you will work closely with business partners, applied scientists, software development engineers, and product managers to accelerate building solutions to expand translation capabilities. You will have significant influence on our overall strategy by helping define science and engineering strategy, define product features, drive system architecture, and spearhead the best-practices that enable a quality product. You will also influence the development processes, and develop well-rounded skills such as leadership, and effective project management. Building a strong development team and developing career plans for the scientists and engineers reporting to you will be a key responsibility. Throughout, you should possess creativity, curiosity, and excellent judgment to thrive in an environment of ambiguity. A day in the life You will spend your days collaborating with scientists, developers, customers, stakeholders, and converting the business needs into a data-driven solution. You will support a team to design and execute science products. You will dive deep into the data and balance technical execution with longer term strategy. You will grow and develop your team. About the team Translation Services is entering a phase where the problems ahead are fundamentally different from the problems we've solved. Our text translation stack is production-grade and serving 30+ language pairs across Retail. But the next frontier — image translation, video subtitle localization, long form text and automated quality evaluation — represents novel research problems at Amazon scale with limited industry precedent.

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!

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.

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.