Careers

Amazon's Customer Experience and Business Trends (CXBT) is looking for an Applied Scientist to help shape the science of evaluating agent-driven builder experiences on AWS. As builders increasingly delegate goals to autonomous coding agents, the agent's experience becomes the builder's experience — and measuring it rigorously is an open research problem. At Amazon Benchmarking, we are developing new scientific methodologies to evaluate how autonomous agents perceive, reason about, and build on AWS. This role sits at the intersection of GenAI, agent evaluation, and developer-experience research. You will design principled measurement methodologies that treat autonomous agents as instruments, capturing behavioral evidence rather than self-reported outcomes and develop rigorous, reproducible techniques for reasoning about why agents make the architectural decisions they do. Evaluating those decisions demands a scientist who understands cloud architecture and services in depth: to reason about the choices an agent makes, you must understand the trade-offs it is navigating. Our team's mission is to constantly challenge the status quo and drive innovation to improve customer experience. This role requires an expert in the areas of GenAI and machine learning with a keen interest in research. The ideal candidate will have experience with deep learning models for natural language processing tasks, agentic/tool-using systems, and the evaluation of autonomous agents, along with a deep working knowledge of cloud architecture and services. A successful candidate will have a passion for clarity, be a self-starter comfortable with ambiguity, have strong attention to detail, be able to work in a fast-paced and entrepreneurial environment and be driven by a desire to innovate. Given the complex nature of the questions we are working on, the ideal candidate will be relentlessly curious and be able to/be interested in having the business conversation underlying the data. Key job responsibilities - Develop novel scientific methodologies for evaluating autonomous, tool-using agents, advancing the state of the art in agent behavioral measurement and developer-experience research. - Design rigorous, reproducible experiments that yield causal insight into agent decision-making, controlling for confounds and ensuring findings are grounded in observed behavior rather than self-report. - Apply deep knowledge of cloud architecture and services to reason about the architectural decisions agents make — the service trade-offs, integration constraints, and deployment considerations that shape what gets built. - Develop verification and synthesis methods that ensure only evidence-grounded conclusions survive, advancing techniques for reliability and faithfulness in agent evaluation. - Design evaluation methodologies and metrics that quantify the quality of agent-produced outcomes across dimensions such as correctness, robustness, and security posture. - Publish and present your work at internal and external scientific venues in the fields of ML. - Collaborate with scientists, engineers, and product managers to design and implement science-focused systems. - Proficiency in model development, validation and implementation for NLP applications. - Excellent verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts. A day in the life As a scientist on the team, you will be involved in every aspect of the process from idea generation, business analysis and scientific research, through development and deployment of advanced models and agentic systems, giving you a real sense of ownership. You'll research, design, and build new methodologies to evaluate the experience of autonomous agents building on AWS — developing the measurement science that lets us reason rigorously about what agents do and why. Besides theoretical analysis and innovation, you will work closely with talented engineers and put your algorithms and models into practice. About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Scientists, Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.

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.

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.

As an Applied Scientist, you will collaborate closely with other scientists and engineers to bring optimization and sequential decision-making research to production. This role combines the scientific application of ML, and specifically optimization, RL, and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel decision policies and optimization models in both prototype and production environments, and to prove their impact through rigorous evaluation and simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of optimization and sequential decision-making solutions spanning constraint programming, stochastic and robust optimization, contextual bandits, and reinforcement learning for real-time MHE control and scheduling optimization in a production environment. • Formulate fulfillment operations and manufacturing scheduling problems (production scheduling, resource allocation, sorter optimization, throughput and congestion control) as optimization or sequential decision-making problems, and design multi-objective functions that balance competing operational objectives such as on-time delivery, utilization, changeover cost, and schedule stability. • Build and leverage high-fidelity simulation and emulation environments for safe offline training, policy validation, and transfer to live systems before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate policies into production planning and real-time control systems, including monitoring, guardrails, and staged rollout. • Communicate results and their limitations clearly in writing to technical and business audiences, and contribute to the team's external research presence through publication where the work merits it. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, Computer Vision (CV), and Physics-Informed Neural Networks (PINNs). The team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The Advertising Trust org is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. Key job responsibilities In this role, you will build and develop AI Agents and ML models to address content understanding problems in Ads and communicating moderation outcomes and advertising policies to advertisers. These agents and models will rely on a variety of multimodal, visual and textual features requiring expertise in these domains. These models need to scale to multiple modalities, languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these agents and models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day. You will also help definite and measure relevant metrics for the business.

Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The Advertising Trust org is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We are also responsible for building a smooth advertiser experience, communicating advertising policies and moderation outcomes effectively and helping advertisers build complaint campaigns. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. Key job responsibilities In this role, you will build and develop Agentic solutions and ML models to address content understanding problems in Ads. These agents and models will rely on a variety of multimodal, visual and textual features requiring expertise in these domains. These models need to scale to multiple modalities, languages and countries. You will collaborate with engineers, product managers, and other scientists to build, train and deploy these models and agents. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.

Team & Project Overview The NBS Data Central team powers analytics, data science, and AI capabilities for Worldwide Global Selling (WWGS). We build scalable data products, and insight-generation systems that drive seller growth across 10+ marketplaces. Seller Intelligence is a P0 foundation theme at the Global Selling level, formed by merging "One Tagging" and "Good Contact" workstreams. It provides seller identity, segmentation, and contact-reach infrastructure that underpins all downstream seller-facing AI workflows — including intelligent outreach, personalized recommendations, and automated engagement. Scope of Impact Own the science pillar for Seller Intelligence within a cross-functional POD (PM + DE + DS + SDE) Directly impact seller engagement metrics across CN, IN, LATAM, and East-Asia expansion regions Models and data products consumed by 5+ downstream teams (ESM, NSR, MKT, NBS AI Ops, ROC) Influence $100M+ annual seller GMS through improved segmentation and contact optimization Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.

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

Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions

Help build Amazon's world-class advertising business by applying machine learning and generative AI to hard problems in campaign optimization. As an Applied Scientist, you'll take models from problem formulation to production, working closely with engineering and product partners to deliver measurable advertiser impact. - Experience training or adapting large language models - Experience with sequential decision-making or optimization under uncertainty - Experience with causal inference or rigorous evaluation of ML systems - Experience designing experiments and performing statistical analysis of results - Experience with modern deep learning frameworks such as PyTorch Key job responsibilities - Lead end-to-end ML initiatives with high ambiguity, scale, and complexity, from problem framing through production deployment - Develop models that improve advertiser outcomes on Amazon's advertising platform - Design and execute experiments and statistical analyses to validate real-world impact - Partner with engineering and product to ship science into customer-facing products responsibly - Advance the state of the art by researching new techniques and applying them to advertising problems About the team You'll join a highly motivated, collaborative, and entrepreneurial team with a broad mandate to experiment, innovate, and break new ground. Your work will directly influence advertiser success and shape the future of Amazon Advertising.