Careers

We are looking for a passionate, talented, and inventive Data Scientist with a strong machine learning and analytics background to help build industry-leading language technology powering Rufus, our AI-driven search and shopping assistant, helping customers with their shopping tasks at every step of their shopping journey. This innovative role focuses on developing and optimizing large language model (LLM)-powered conversational experiences. The core emphasis is to get the best performance out of state-of-the-art LLMs via careful and methodical instruction design, contextual grounding, informed choices of MCP tools and agent/multi-agent systems, evaluation frameworks, and experimentation to systematically improve LLM quality, robustness, and customer impact. The work combines scientific rigor with product intuition to systematically raise the bar for conversational AI performance at Amazon scale. Our mission in conversational shopping is to make it easy for customers to find and discover the best products to meet their needs by helping with their product research, providing comparisons and recommendations, answering product questions, enabling shopping directly from images or videos, providing visual inspiration, and more. We do this by leveraging advanced analytics, Natural Language Processing (NLP), Machine Learning (ML), A/B testing, causal inference, and data-driven insights to continuously improve our systems. Key job responsibilities As a Data Scientist on our team, you will develop and maintain LLM instructions iterations and evaluation frameworks, including automated eval pipelines, LLM-as-a-judge methodologies, rubric design, and dataset curation to measure nuanced aspects of response quality. You will partner with the wider org to experiment with techniques such as retrieval augmentation, context enrichment, prompt decomposition, and model fine-tuning or post-training strategies, if and when applicable. You will leverage petabytes of data and identify opportunities to leverage machine learning models aimed at making conversational systems more performant. A day in the life You will: Perform hands-on analysis of large-scale multimodal interaction datasets to develop insights into how customers engage with conversational AI systems and how to improve response quality and customer experience. Use statistical methods, experimentation, and data-driven analysis to develop scalable approaches for measuring, evaluating, and optimizing large language model (LLM)-based shopping assistant systems, leveraging structured and unstructured contextual signals. Design and analyze A/B tests and experiments to evaluate new features and model improvements, ensuring statistical rigor and actionable insights. Develop metrics, dashboards, and reporting frameworks to monitor system performance, customer engagement, and business impact. Conduct deep-dive analyses to identify opportunities for improving conversational relevance, grounding, customer satisfaction, and downstream business impact. Collaborate with Applied Scientists and Engineers to translate analytical insights into production systems, working closely on model evaluation and deployment. Establish automated processes for large-scale data analysis, ETL pipelines, metric generation, and experimentation frameworks. Communicate results and insights to both technical and non-technical audiences, including through presentations, written reports, and data visualizations. About the team The Rufus Features Science team, based in London, works alongside ~150 engineers, designers and product managers, shaping the future of AI-driven shopping experiences at Amazon. The team works on every aspect of the Rufus AI, from making Rufus agentic, enabling customers to set price alerts or empower Rufus to act on their behalf and automatically purchase products when the price is right, to understanding multimodal user queries and generating answers that combine text, image, audio and video, including deep research reports that scour the web and the Amazon catalog to provide detailed and personalised shopping guidance. We utilize and advance state-of-art techniques in the fields of Natural Language Processing, gen AI, Information Retrieval, Machine/Deep Learning, and Data Mining. We validate our work by actively participating in the internal and external scientific communities.

Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies

The North America Stores GenAI Evaluation Media (GEM) team is seeking a Senior Applied Scientist to help shape the future of visual shopping experiences. We're building CXs and foundational capabilities to understand, enhance, and generate real-time GenAI imagery, videos and CXs that inspire customers and drive purchase confidence, towards our vision to be the leader in visual media. Specifically, the charter will focus on visual agentic experiences, multi-modal personalization, and real-time image/video generation, looking ahead as customer shopping continues to inspirational assistant-driven experiences. As a Senior Applied Scientist on the team, you will own and define the scientific vision, strategy, and roadmap for agentic AI capabilities that inform and guide the customer's shopping journey through visuals. This includes architecting and advancing core science primitives for multimodal understanding, visual content generation and editing, personalized virtual try-on, and automated quality assurance. You will establish the technical direction for foundational capabilities that enable customers to express and discover styles through multimodal conversation and receive personalized, visual responses that bring their ideas to life. Your scientific leadership will emphasize accurate, real-time visual understanding and generation, contextual understanding, and scalable personalization, enabling agentic AI to actively collaborate with customers to achieve their style goals. You will set the long-term research agenda, bringing together computer vision, natural language processing, generative AI, and human-centered design to create agentic shopping experiences that are as intuitive as talking to a human specialist with a deep domain knowledge base. Success requires defining and institutionalizing robust evaluation frameworks and metrics, influencing and aligning cross-functional partners across organizations, validating asset effectiveness across diverse customer touch points, identifying whitespace opportunities, and staying at the forefront of rapid advances in AI technology. The ideal candidate will have deep and broad technical expertise in Computer Vision, Generative AI, or related fields with a proven track record of connecting scientific work to customer and business outcomes at scale. You will serve as a technical leader and thought partner to scientists, engineers, and senior stakeholders across Amazon, mentoring junior scientists, raising the scientific bar, and delivering innovation while upholding a culture of scientific excellence and customer obsession. This role requires both rigorous research skills and practical engineering instincts, with a focus on delivering solutions that scale and a demonstrated ability to navigate ambiguity, make high-judgment trade-offs, and drive alignment across competing priorities. You will be expected to contribute to the broader scientific community through publications, patents, and internal knowledge sharing. This is a unique opportunity to shape the technical strategy for visual commerce through applied AI research, building the systems that will define how hundreds of millions of customers discover and evaluate products and styles through visual experiences. Key job responsibilities Innovation & Technical Execution Define the research roadmap and advance core science primitives for vision and language understanding, visual content generation and editing, virtual try-on, and automated quality assurance via state-of-the-art computer vision, machine learning, and generative AI Architect visual agentic systems, making high-judgment trade-offs across visual quality, relevance, latency, cost, and long-term extensibility Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives, institutionalizing rigorous validation across customer touch points Own end-to-end delivery of complex, ambiguous research initiatives from problem formulation through experimentation to production deployment, with minimal guidance Identify whitespace opportunities by staying at the forefront of AI/ML advances and translating them into actionable research directions with clear customer and business impact Drive development and deployment of scalable agentic systems for visual content understanding and generation, ensuring architectural decisions support long-term platform evolution Set and continuously raise the scientific and engineering bar across the team Tackle the team's most complex technical problems while maintaining practical focus on customer value and solution generalizability Advance the team's scientific reputation through high-impact publications and presentations at top-tier internal and external venues, and generate intellectual property through patents Cross-functional Influence & Leadership Influence product and engineering roadmaps by partnering with senior leadership to shape customer-facing features grounded in scientific insight Drive technical alignment across multiple teams and organizations within Amazon, resolving ambiguity and building consensus on approaches Communicate research vision, findings, and technical trade-offs persuasively to executive, technical, and non-technical stakeholders, shaping investment decisions Mentor and develop junior and mid-level scientists, accelerating their growth and impact

We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve large-scale real world challenges, and provide visible benefit to end-users, this is your opportunity. Come work on the Amazon Prime Air Team! We're looking for an applied scientist who combines superb technical, research and analytical capabilities with a demonstrated ability to get the right things done quickly and effectively. We’re looking for someone who innovates and loves solving hard problems. You will work hard, have fun, and of course, make history! The monthly gross salary according to the CBA is at least EUR 4.006. There is a willingness to make an overpayment, depending on qualification and professional experience. Export Control License: This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf. Key job responsibilities Develop computer vision algorithms for autonomous drones. Monitor systems in operation. Manage ML ops pipelines. Deep dive into data. Build prototypes. Port algorithms to real-time systems. About the team The Perception team of Prime Air develops Computer Vision algorithms that allow our drones to sense and avoid obstacles, allowing to fully autonomously deliver packages to customers in 30 minutes or less.

Economists at Amazon are expected to work directly with our senior management and scientists from other fields on key business problems faced across Amazon. We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Collaborate with economists, data scientists, financial managers, and business leaders to define product requirements, provide science support, and communicate feedback. Implement economics methods to solve specific business problems utilizing code (Python, R, Scala, etc.). Improve existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. Presenting data in a format that is immediately useful to answer the critical business questions. About the team The Perfect Order Experience (POE) Econ team serves as POE teams' trusted economics partner, enhancing business strategy and operational effectiveness across POE and Selling Partner Services (SPS) through economic analysis and insights. We focus on advancing POE's goals towards the perfect order experience vision while delivering value to broader teams where strategic alignment exists. Through rigorous analytical frameworks, we help leaders navigate complex business and operational challenges. We embrace AI to revolutionize how we work and amplify our strategic contributions.

Amazon Science gives you insight into the company’s approach to customer focused scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. Please visit https://www.amazon.science for more information. Are you an expert in Natural Language Processing (NLP) and Large Language Models (LLM)? Are you interested in building Generative AI solutions on complex business problems that have significant global benefit. The Brand Protection team designs and builds high performance AI systems using machine learning that identify and prevent abuse and counterfeit on behalf of brand owners worldwide. We are looking for a highly talented scientist to help build of our vision for Brand Protection. As a senior applied scientist on the team, you will use NLP and LLM techniques to understand and extract key information from product detail page, built automated AI solutions that thinks like human to assist decision making and eventually make autonomous decisions. You will work backwards from data insights and customer feedback to build the right machine learning solutions, and resourceful in finding innovative solutions to unsolved problems. You will work closely will product team and engineering partners to launch the solution into production and owning the end-to-end solution. An ideal candidate should have extensive experience driving Machine Learning initiatives, specially in NLP and LLM applications, from conception to launch in a rapidly evolving environment. Amazon’s growth requires leaders who move fast, have an entrepreneurial spirit to create new solutions, have an unrelenting tenacity to get things done, and are capable of breaking down and solving complex problems. Major responsibilities: - Understand business challenges by analyzing data and customer feedback - Collaborate with tech and product teams on building ML strategies, experimentation, implementation and continuous improvement - Analyze and extract relevant information from large amounts of both structured and unstructured data to design strategies to solve business problems. - Use NLP, LLM and machine learning techniques to create scalable solutions for business problems - Create business and analytics reports and present to the senior management teams - Research and implement novel AI solutions and publish research papers About the team Here at Selling Partner Services, we embrace our differences. We are committed to furthering our culture of inclusion. We have 14 employee-led affinity groups, reaching 10,000+ employees in chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our DEI Ambassador Program. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Amazon Science gives you insight into the company’s approach to customer focused scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. Please visit https://www.amazon.science for more information. Are you an expert in Natural Language Processing (NLP), computer vision Large Language Models (LLM) and multi-modality? Are you interested in building Generative AI solutions on complex business problems that have significant global benefit. The Brand Protection Science team designs and builds high performance AI systems using machine learning and deep learning that identify and prevent infringement and counterfeit on behalf of brand owners worldwide. We are looking for a highly talented scientist to help build of our AI vision for Brand Protection. As a applied scientist on the team, you will use STOA AI and ML techniques to understand and extract key information from product detail page, built automated AI solutions that thinks like human to make autonomous decisions. You will work backwards from data insights and customer feedback to build the right machine learning solutions, and resourceful in finding innovative solutions to unsolved problems. You will work closely will product team and engineering partners to launch the solution into production and own the end-to-end solution. An ideal candidate should be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied scientist, you will own the design and development of end-to-end AI solutions, from conception to launch in a rapidly evolving environment. You’ll have the opportunity to create science roadmaps, and drive production level projects that will support Amazon Science. extensive experience driving Machine Learning initiatives, specially in NLP and LLM applications, from conception to launch in a rapidly evolving environment. You will work closely with other scientists and enigneers to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Major responsibilities: - Understand business challenges by analyzing data and customer feedback - Collaborate with tech and product teams on building ML strategies, experimentation, implementation and continuous improvement - Analyze and extract relevant information from large amounts of both structured and unstructured data to design strategies to solve business problems. - Use deep learning and machine learning techniques to create scalable solutions for business problems - Create business and analytics reports and present to the senior management teams - Research and implement novel AI solutions and publish research papers Key job responsibilities Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.

Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Lead design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation

The Fulfillment by Amazon (FBA) Science team is looking for a passionate, curious, and creative Senior Research Scientist with deep expertise in statistical modeling, machine learning, and large language models (LLMs), and a proven record of solving complex forecasting problems at scale. Our team sits at the intersection of supply chain science, seller behavior modeling, and policy analytics — building the forecasting backbone that powers FBA's shipment creation, inbound arrival planning, and inventory management. We develop science solutions that predict seller shipment creation patterns, model inbound arrival timing and quantity, and forecast inventory levels across Amazon's fulfillment network. A key challenge we tackle is understanding how seller behavior changes — driven by market dynamics, FBA policy updates, and incentive structures — and how these behavioral shifts propagate into forecasting signals. We aim to build forecasting systems that are not only accurate but also explainable and actionable for both internal stakeholders and sellers. To do so, we build and innovate science solutions at the intersection of statistical learning, machine learning, econometrics, operations research, and generative AI. As a Senior Research Scientist, you will propose and deploy solutions drawing from a range of scientific areas including time-series forecasting, causal inference, Bayesian methods, LLMs, and deep learning. This role has high visibility to senior Amazon business leaders and involves close collaboration with scientists, engineers, and product teams to integrate scientific work into production systems. Key job responsibilities - As a senior member of the FBA Science forecasting team, play an integral role in building and advancing Amazon's FBA shipment creation, inbound arrival, and inventory forecasting systems. - Research and develop statistical models, ML models, and LLM-based solutions to forecast seller shipment creation behavior, inbound arrival patterns, and downstream inventory levels across the FBA network. - Model and quantify the impact of seller behavior changes and FBA policy updates (e.g., capacity limits, fee structures, inbound placement policies) on forecasting accuracy, and develop robust forecasting approaches that adapt to these dynamics. - Build explainability frameworks for forecasting models — enabling science teams, product managers, and business stakeholders to understand model drivers, diagnose forecast errors, and trust model outputs. - Define a long-term science vision and roadmap for the forecasting team, driven fundamentally by customer and seller needs, translating those directions into specific plans for research and applied scientists, as well as engineering and product teams. - Drive and execute forecasting science projects end-to-end: from ideation, analysis, and prototyping through to development, deployment, metrics definition, and monitoring. - Review and audit modeling processes and results for other scientists, both junior and senior. - Advocate the right science solutions to business stakeholders, engineering teams, and executive-level decision makers. A day in the life In this role, you will be a technical leader in forecasting science with significant scope, impact, and high visibility. Your solutions will directly influence billions of dollars in inventory decisions, inbound logistics planning, and seller experience across Amazon's global fulfillment network. As a senior scientist on the team, you will be involved in every aspect of the process — from idea generation, business analysis, and scientific research, through to development and deployment of advanced forecasting models — giving you a real sense of ownership. From day one, you will work with experienced scientists, engineers, and product designers who are passionate about what they do. You are expected to make decisions about modeling methodology, technology choices, and explainability approaches. You will strive for simplicity and demonstrate judgment backed by mathematical rigor. You will also collaborate with the broader decision and research science community at Amazon to broaden the horizon of your work, and mentor engineers and scientists. We are seeking someone who wants to lead projects requiring innovative thinking and deep technical problem-solving skills to create production-ready forecasting solutions. The candidate will need to be entrepreneurial, wear many hats, and work in a fast-paced, high-energy, highly collaborative environment. About the team Fulfillment by Amazon (FBA) is a service that allows sellers to outsource order fulfillment to Amazon, enabling them to leverage Amazon's world-class fulfillment infrastructure to deliver on the Prime promise. FBA ships more than half of all products offered on Amazon, and our science team is at the heart of making that possible. The FBA Science forecasting team focuses on predicting seller shipment creation, inbound arrival, and inventory dynamics — providing the signals that drive capacity planning, inbound logistics, and inventory positioning across the network. We work full-stack, from foundational forecasting models to seller-facing explainability tools. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making.

The Kindle team is seeking innovative Applied Scientists for improving the reading experience. Our team is dedicated to enhancing the book reading experience using advancements in Science to improve the book reading experience for Kindle customers. Key job responsibilities - Inspect science initiatives across Amazon to identify how these can be applied and scaled to book reading experience. - Participate in team design, scoping and prioritization discussions. You must be able to map a business goal to a scientific problem, and map business metrics to technical metrics. - Spearhead the design and implementation of new features and algorithms based on thorough research and collaboration with cross-functional teams. - You have expertise in one of the applied science disciplines, such as machine learning, natural language processing, computer vision, Deep learning - You are able to use reasonable assumptions, data, and customer requirements to solve problems. - You initiate the design, development, execution, and implementation of smaller components with input and guidance from team members. - You work with SDEs to deliver solutions into production to benefit customers or an area of the business. - You assume responsibility for the code in your components. You write secure, stable, testable, maintainable code with minimal defects. - You understand basic data structures, algorithms, model evaluation techniques, performance, and optimality tradeoffs. - You follow engineering and scientific method best practices. You get your designs, models, and code reviewed. You test your code and models thoroughly - You participate in team design, scoping and prioritization discussions. You are able to map a business goal to a scientific problem and map business metrics to technical metrics. - You invent, refine and develop your solutions to ensure they are meeting customer needs and team goals. - You keep current with research trends in your area of expertise and scrutinize your results. A day in the life You will solve customer problems through innovative solutions that leverage the advancements in science. You will work with a group of talented scientists on researching algorithm and running experiments to test solutions to improve our experience. This will involve collaboration with partner teams including engineering, PMs, and other scientists to discuss data quality, model development and productionizing the same. You will mentor other scientists, review and guide their work, help develop roadmaps for the team.