Careers

Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? We are looking for a Data Scientist who will be responsible to develop cutting-edge scientific solutions to optimize our fulfillment strategy across multiple regions of the world (EU, JP, IN and more), to maximize our Customer Experience and minimize our cost and carbon footprint. You will partner with the worldwide scientific community to help design the optimal fulfillment strategy for Amazon. You will also collaborate with technical teams to develop optimization tools for network flow planning and execution systems. Finally, you will also work with business and operational stakeholders to influence their strategy and gather inputs to solve problems. To be successful in the role, you will need deep analytical skills and a strong scientific background. The role also requires excellent communication skills, and an ability to influence across business functions at different levels. You will work in a fast-paced environment that requires you to be detail-oriented and comfortable in working with technical, business and technical teams. Key job responsibilities - Design and develop mathematical models to optimize inventory placement and product flows. - Design and develop statistical and optimization models for planning Supply Chain under uncertainty. - Manage several, high impact projects simultaneously. - Consult and collaborate with business and technical stakeholders across multiple teams to define new opportunities to optimize our Supply Chain. - Communicate data-driven insights and recommendations to diverse senior stakeholders through technical and/or business papers. - Leverage LLMs to improve explainability of our optimization solutions and drive engagement from supply chain planners across the world.

Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for an Applied Science Manager who will take care of the Content Understanding for Prime Video. Content Understanding team helps Prime Video "see" and "understand" what's actually happening inside its videos - the characters, scenes, dialogue, events, and visual elements. We build the systems that analyze video content so that we can build customer experiences on top of our work. This manager will also lead a team of scientists and engineers who are building the next generation of video understanding and search for Prime Video. You will own the strategy, execution, and delivery of systems that help machines watch, describe, and find content across the largest streaming catalog in the world. Key job responsibilities As an Applied Science Manager, you will: - Define and drive the technical direction and roadmap across our "understanding" and "search" workstreams — making sure our work connects to Prime Video's broader goals around content intelligence. - Lead and grow a high-performing team of scientists and engineers, building a culture of scientific excellence, customer focus, and reliable delivery. - Partner with teams across Prime Video (personalization, search etc) to understand what they need from us, shape our outputs to be reliable building blocks for their products, and drive adoption. - Own delivery end-to-end — from early research and experimentation all the way through launching production systems that run at the scale of Prime Video's full catalog. - Define and track success metrics for quality, reliability, and real-world impact on downstream products, continuously raising the bar on what "good" looks like. - Manage ambiguity across a broad set of workstreams, make clear prioritization decisions, and communicate trade-offs effectively to senior leadership. About the team The Prime Video - Content Localization, Understanding and Enrichment team's mission is to deeply understand content to automate & scale existing solutions, and launch new experiences across Prime Video while accelerating science outputs & forward-investing in science. This manager will lead Content Understanding that defines content at a fundamental scene-level by generating & maintaining a comprehensive set of Content Understanding attributes which are leveraged across Prime Video for their varied use-cases; ranging from content moderation to metadata generation, ad placement identification, etc.

At Amazon Robotics, we design advanced robotic systems capable of intelligent perception, learning, and action alongside humans, at massive scale. Our mission is to deploy robots that increase productivity and efficiency across Amazon fulfillment centers while operating safely and robustly in complex, contact-rich environments. We are seeking an Applied Scientist to develop manipulation controllers for robotic systems operating in contact-rich, uncertain environments. In this role, you will design force-aware control strategies grounded in impedance/admittance frameworks and augment them with data-driven policy learning to achieve robust, adaptive manipulation behaviors. You will combine physics-based modeling, control-theoretic design, and machine learning to build manipulation capabilities that generalize across objects, tasks, and operational conditions. You will collaborate closely with experts in perception, machine learning, motion planning, controls, and software engineering to deliver solutions that perform reliably on real hardware at production scale. As part of this role, you will study and extend relevant academic and industry research in robot learning and manipulation, prototype and validate learned policies in simulation and on hardware, and transition successful approaches into production systems. Successful candidates demonstrate strong intuition for physical systems, experience applying ML to robotics problems, and the ability to reason about failure modes, edge cases, and deployment constraints in contact-rich manipulation. Clear communication, hands-on experimentation, and a bias toward practical impact are essential. Key job responsibilities - Research, design, implement, and evaluate machine learning–based manipulation policies for contact-rich tasks, integrating learning with feedback control, estimation, and motion planning. - Develop learning frameworks that leverage simulation, real-world data, and hybrid physics- and data-driven models to enable robust agency interaction, grasping, insertion, and object handling. - Design and execute experiments in simulation and on hardware to train, validate, and stress-test learned manipulation policies under real-world variability and uncertainty. - Collaborate with software engineering teams to deliver scalable, real-time, and maintainable implementations of learning-based manipulation algorithms in production robotic systems. - Partner with cross-functional teams across perception, hardware, systems engineering, science, and operations to transition learned policies from research prototypes to reliable, production-ready capabilities across Amazon Robotics platforms. 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!

Help us build the software stack underneath Frontier AI & Robotics: the layer between hardware and the AI policies running on it. You’ll work end-to-end across the stack, from bringing up sensors and actuators at the HW/SW boundary, to hardening the multi-process runtime that ties them together, to shipping the framework changes that let the rest of the team move faster. This is a generalist role with a real systems edge. We need someone equally at home down at the sensor and OS boundary (drivers, IPC, real-time behavior, profiling) and up in robotics application code (most of it Python). You’ll spend stretches of a few days to a few weeks each on things like bringing up a new sensor at the protocol layer, chasing a flaky USB camera at the kernel boundary, generalizing the camera stack, cutting perception latency, porting code onto edge compute, or chasing a DDS/ROS 2 performance regression. You’ll work with sharp, motivated engineers across hardware, ML research, controls, and ops on continuously changing problems at the frontier of AI and robotics. The role rewards engineers who communicate clearly, invest in cross-team relationships, and ship. Key job responsibilities - Bring up and integrate sensors, actuators, and edge compute across diverse robotics hardware platforms. - Build and improve the multi-process Python and ROS 2 runtime: process management, IPC, observability, lifecycle. - Diagnose and fix bottlenecks across the stack: middleware performance (DDS, ROS 2), perception pipeline latency, edge inference throughput, system-level resource contention. - Generalize one-off solutions into reusable infrastructure (camera stacks, telemetry pipelines, edge deployment tooling) that scales across hardware platforms and use cases. - Port code onto the constrained edge-compute platforms our robots run. Make it work, then make it work well. - Identify gaps in the team’s velocity, whether that’s a missing piece of infrastructure or a fragile sensor that nobody has time to fix, and close them. - Make proper tradeoffs between prototyping and production software. About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.

We're looking for an Applied Scientist to develop computer vision and machine learning models that keep Amazon's workforce safe. Your research and models will be deployed across hundreds of operations facilities globally, helping to reduce safety incidents for over 1.5 million people. You'll join a team where science meets real-world impact. You'll design and train models for tasks like activity recognition, anomaly detection, object detection, and risk prediction using video, image, and sensor data from Amazon's operational environments. You'll work closely with software engineers to take your models from experimentation through production deployment at scale. If you're excited about applying advanced ML research to a problem that genuinely improves people's lives, and you thrive in an environment where your work ships to production, not just to a paper, this is the role for you. Key job responsibilities - Design, develop, and deploy computer vision and machine learning models for workplace safety applications (e.g., activity recognition, anomaly detection, pose estimation, object detection) - Develop and iterate on model architectures using deep learning frameworks, running experiments on large-scale video, image, and sensor datasets - Collaborate with software engineers to productionize models - optimizing for inference latency, accuracy, and reliability in edge and cloud environments - Analyze operational data to identify patterns and signals indicating safety risks, and translate findings into actionable model improvements - Stay current with the latest research in computer vision, deep learning, and related fields, and evaluate applicability to safety use cases - Communicate findings and technical approaches clearly to both technical and non-technical stakeholders through documents, presentations, and design reviews - Contribute to the team's scientific culture through code reviews, knowledge sharing, and mentorship About the team Amazon's Workplace Health & Safety (WHS) organization is responsible for keeping over 1.5 million employees safe across our global retail operations. Within WHS, our technology team builds the science and engineering capabilities that power Amazon's safety strategy at scale. We're a cross-functional group of applied scientists, software engineers, data engineers, and technical program managers developing computer vision systems, generative AI applications, sensor and IoT solutions, and analytics platforms - all aimed at reducing workplace injuries. As an applied scientist here, you'll partner directly with engineers who build the production infrastructure for your models, and with safety domain experts who ground your work in real operational needs. Our culture values scientific rigor, fast iteration, and shipping models that create measurable safety outcomes.

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers 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.

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Key job responsibilities As a Sr. Applied Scientist, you will - Help shape the scientific direction of the organization by proposing state of the art modeling approaches, driving experimentation, and balancing scientific rigor with execution speed to deliver measurable customer impact. - Think strategically about the future of GenAI and multimodal AI, identifying opportunities to transform music understanding, curation, and engagement. - Stay at the forefront of advancements in GenAI, recommendation systems, and large-scale machine learning, driving adoption of new techniques where they create meaningful customer value. - Collaborate closely with engineers across Music Intelligence, Personalization, Search and other partner teams to support long-term product and CX goals. - Mentor applied scientists and engineers while actively contributing to the broader science and ML community across Amazon Music. - Produce clear and concise technical documentation outlining methodologies, design decisions, trade-offs, experiment results, and customer impact. - Invent and scale innovative AI/ML solutions for complex music intelligence, personalization, metadata quality, and content understanding problems. - Drive the design of scientifically sophisticated ML systems and platforms, contributing core technical innovation and providing organization-wide architectural guidance. - Define the long-term science vision and roadmap for Amazon Music AI initiatives, translating customer needs into actionable plans for science and engineering teams. - Partner closely with engineering and product teams to build and launch scalable AI solutions that improve music discovery, personalization, and customer experience. - Lead rigorous experimentation and data-driven evaluation, including large-scale A/B testing, to measure and optimize customer impact. - Communicate complex scientific concepts clearly to technical and business stakeholders, including senior leadership. - Mentor scientists and engineers, fostering a culture of innovation, technical excellence, and strong customer focus. About the team The Amazon Music – Catalog Team develops sophisticated models for understanding music across multiple dimensions: sonic, thematic, cultural, lyrical, etc. This team aims to unify this deep music knowledge that will power intelligent music experiences across Amazon Music. Ultimately, the goal of our team is to delivery a musically credible experience, which will help grow engagement across all customers, but also delight the fans!

If you have ever bought or sold anything on Amazon, you have touched Amazon Marketplace. Amazon’s Marketplace business is one of the largest in the world. We are now in 23 countries. We are growing fast, with customers in many more countries. Amazon’s platform is the engine that powers Amazon’s Marketplace businesses, and Sellers rely on this platform and our support to start selling on Amazon and to grow their business. Amazon Marketplace enables millions of Sellers worldwide to list hundreds of millions of products and manage orders for inventory across dozens of different categories and languages. While working with millions of Sellers worldwide, we constantly strive to improve the selection for Customers and the capabilities of our platform for Sellers. The Seller Fulfillment Services (SFS) team is looking for a motivated and innovative Applied Scientist with strong analytical skills and practical experience to join our science team. As a key member of the SFS science team, you will provide expertise that helps accelerate the business. You will build science solutions that will help us to provide our customers with the largest selection of merchants at the lowest, and the most reliable delivery service regardless of the seller. You will research, design and improve on the models that will impact Amazon’s customer directly. You will be working in a highly collaborative environment partnering with various science, product management, engineering, operations, finance, business intelligence and analytics teams to develop science models to solve business problems. You will need to understand the business requirements and translate them into complex analytical outputs. You will design tests to explain performance of the models from impact on customer and cost perspective. You will create ML models to capture features impacting performance. You should be comfortable building prototypes, testing and improving them given the feedback from the real time data. You should be able to present your model and findings to a various range of stakeholders. Looking for candidate with expertise in the areas of machine learning, operations research, and statistics. With expertise in applying theoretical models in an applied environment relying heavily on the latest advances in machine learning, optimization, stochastic modeling, and engineering. The candidate will be expected to work on numerous aspects, such as feature engineering, modeling, probabilistic modeling, hyper-parameter tuning, scalable inference methods and latent variable models. Challenges will involve dealing with very large data sets and requirements on throughput. Key job responsibilities - Design, implement, test, deploy, and maintain innovative science solutions to accelerate our business. - Create experiments and prototype implementations of new learning algorithms and prediction techniques - Collaborate with scientists, engineers, product managers, and stakeholders to design and implement software solutions for science problems - Use best practices to ensure a high standard of quality for all of the team deliverables

Come build the future of entertainment with us. Are you interested in shaping the future of movies and television? Do you want to define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 200 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. The Observability and Triage team is looking for an Applied Scientist for our London office experienced in generative AI and large models. This is a wide impact role working with development teams across the UK, India, and the US. This greenfield project will deliver features that reduce the operational load for internal Prime Video builders and for this, you will develop AI-driven solutions that automatically detect anomalies, identify root causes, recommend resolution paths and take action for operational incidents. We consume petabytes of data daily across multiple metric, log and data based events and you would be experimenting on how to shape the future through this data. You will have strong technical ability, excellent teamwork and communication skills, and a strong motivation to deliver customer value from your research. Our position offers opportunities to grow your technical and non-technical skills and make a global impact. Key job responsibilities - Design and develop machine learning and generative AI systems for automated incident triage, root cause analysis, and resolution recommendation at scale - Rapidly prototype and evaluate hypotheses in a high-ambiguity environment, leveraging both quantitative experimentation and domain expertise in operational systems - Build evaluation frameworks (including LLM-as-a-Judge approaches) to measure model accuracy across triage accuracy and root cause prediction - Collaborate with software engineering teams to integrate ML models into production observability systems serving hundreds of development teams - Communicate results and insights to both technical and non-technical audiences, including through publications, presentations, and written reports A day in the life On a typical day, you analyse patterns across thousands of operational incidents to improve an automated triage model, then design an experiment to test whether a new Generative-AI based approach better identifies root causes for complex multi-service incidents. Your internal customers are Prime Video development teams who rely on your solutions to reduce the time and effort spent responding to operational events. You will collaborate closely with software engineers, and operational stakeholders across the world to ensure your research translates into production systems that measurably remove customer impact. About the team Our team builds AI-powered observability and triage solutions for Prime Video development teams, consuming petabytes of data daily to automatically detect, diagnose, and recommend resolutions for operational incidents.