Careers

Do you want to shape the next generation of AI-driven customer experiences for Amazon’s most ground-breaking products? Join us and help invent the future of shopping. We are seeking a passionate, innovative, and highly skilled Applied Scientist with expertise in AI, Agentic LLMs, Generative AI, Machine Learning, and NLP to help build LLM-powered solutions for Amazon’s BuyForMe product, which enables Amazon customers to discover and purchase products from any merchants through browser use agents. Our team develops science and agentic AI capabilities that power a seamless, end-to-end shopping experience for Amazon customers. We build and advance technologies such as web agent frameworks, LLM fine-tuning, reinforcement learning, context engineering, RAG, MCP, and automated benchmarking to improve pre-purchase, in-purchase, and post-purchase workflows. Key job responsibilities As an Applied Scientist on our team, you will: Invent, implement, and evaluate state-of-the-art models and agentic systems that directly impact customer experience. Conduct research that may lead to publications, patents, or cross-Amazon technical influence. Collaborate with engineers, product managers, and other scientists to translate business challenges into scalable science solutions. Run experiments with real customer data and validate hypotheses in a high-impact product environment. Drive excellence in model performance, safety, reliability, and evaluation frameworks.

Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people who are passionate about making a meaningful impact on communities and the environment while helping shape the future of sustainable business practices. The Worldwide Sustainability (WWS) organization capitalizes on Amazon's scale and speed to build a more resilient and sustainable company. We manage our social and environmental impacts globally and drive solutions that enable our customers, businesses, and the world to become more sustainable. Through innovative programs and strategic partnerships, we're creating lasting positive change in the communities where we operate while advancing Amazon's commitment to environmental stewardship and social responsibility. We are looking for a robotics scientist to build and operate the first autonomous materials discovery laboratory at Amazon. This role combines deep robotics expertise (motion planning, control, platform integration) with modern Physical AI approaches (vision-language-action models, sim-to-real transfer, agentic orchestration). You will design autonomous experimental workflows that integrate dexterous robotic platforms, analytical instruments, and AI-driven hypothesis generation into a closed-loop discovery pipeline — where foundation models drive hypothesis generation and experimental planning, validated on real hardware under real chemistry. This is not a pure research role. You will work directly with physical robots, laboratory instruments, and deployment pipelines. The work is expected to be published, but the primary measure of success is a working autonomous platform that generates scientific results. Materials science expertise is not required — the team includes domain scientists. What matters is strong AI and robotics foundations, scientific curiosity, and the drive to ship. Key job responsibilities - Develop, train, and benchmark robotic manipulation policies for materials synthesis and characterization using modern policy architectures (VLA architectures, diffusion policies). - Design and execute sim-to-real transfer strategies including domain randomization, physics parameter tuning, and visual domain adaptation for laboratory robotic systems. - Integrate robotic platforms and laboratory instruments into automated workflows via APIs (SiLA 2, or equivalent), building real-time data pipelines for multimodal experimental outputs. - Architect policy training pipelines combining teleoperation data, synthetic demonstrations, reinforcement learning, and imitation learning for dexterous lab manipulation. - Build production-grade agentic runtime systems — failure detection, retry logic, exception handling, and human-handoff protocols — for unattended experimental sessions. - Design and execute autonomous experimental campaigns applying active learning, Bayesian optimization, or RL to drive iterative materials discovery. - Drive technical design reviews and set scientific direction for the autonomous lab platform. A day in the life You build the Physical AI systems that power robotics in autonomous science lab, one where foundation models generate hypotheses, robots execute experiments, and closed-loop optimization discovers materials that did not exist yesterday. You train manipulation policies in simulation, transfer them to a physical cobot, and watch real chemistry validate (or invalidate) an AI-generated theory. The signal here is not a metric on a dashboard; it is a synthesizing and testing novel material with measurable sustainability impact. If you want your research to have physical weight, this is the lab. About the team Sustainability Science and Innovation (SSI) is a multi-disciplinary research team within WW Sustainability combining science, ML, economics, and engineering. The autonomous laboratory is a new capability being built from the ground up. You will work alongside computational materials scientists, chemists, and ML engineers — with access to AWS-scale compute and Amazon's supply chain for hardware. The work targets sustainability outcomes across packaging, building materials, and alternative fuels.

Most AI assistants today start every conversation from scratch, forgetting what they learned yesterday about developer's codebase, patterns, and the way they like to work. Amazon Web Services (AWS) is assembling an elite team of scientists and engineers to change that, and we are hiring an Applied Scientist to build the systems that let our AI development tools accumulate knowledge over time and get genuinely better the more you use them. You will help create generative AI technologies, including Kiro IDE and CLI, that are transforming how software gets built. You will take on some of the hardest open problems in machine learning: how a model keeps learning without forgetting what it already knew, how to store and retrieve the right memory at the right moment, and how to personalize the experience for each developer and team without starting over every time. Your work will reach millions of developers rather than sit in a paper, and you will own a research direction end to end rather than share a sliver of attention across many priorities. Key job responsibilities - Push the boundaries of continual and lifelong learning for large language models specialized in code intelligence - Design memory-augmented architectures and retrieval mechanisms that let the assistant consolidate, recall, and personalize knowledge over time - Invent and implement state-of-the-art machine learning solutions that operate at unprecedented Amazon scale - Deploy products that directly impact the daily workflows of millions of developers worldwide - Publish and present your work at premier ML and NLP conferences (NeurIPS, ICML, ICLR, ACL, EMNLP) - Accelerate innovation by working directly with customers to rapidly transition research breakthroughs into production systems About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

The best coding assistants have to do two hard things together: understand code as the structured, interconnected system it really is, and keep learning from every interaction so they get better over time. Most AI tools today do neither well. They flatten code into tokens and lose the structure that makes software navigable, and they start each session from scratch, forgetting what they learned yesterday about a developer's codebase, patterns, and way of working. Amazon Web Services (AWS) is assembling an elite team of scientists and engineers to change both at once, and we are hiring an Applied Scientist who will work across code understanding and continual learning as a single, connected problem. You will help create generative AI technologies, including Kiro IDE and CLI, that are transforming how software gets built. In this role you will turn the many signals around code, from static analysis and symbol graphs to build systems and language servers, into a unified representation our models can reason over, and you will make that system learn over time, so it accumulates knowledge without forgetting what it already knew, retrieves the right memory at the right moment, and personalizes the experience for each developer and team. These are not two separate jobs. A system that understands code deeply is also the one best positioned to remember and personalize how it helps, and you will own that intersection end to end. Your work will reach millions of developers rather than sit in a paper. Key job responsibilities - Build representations of code that fuse static analysis, symbol and call graphs, type information, and build system context, and that models can use at repository scale - Make those systems learn over time through memory-augmented architectures, retrieval mechanisms, and personalization that consolidate and recall knowledge across sessions - Invent and implement state-of-the-art machine learning solutions that operate at unprecedented Amazon scale - Deploy products that directly impact the daily workflows of millions of developers worldwide - Publish and present your work at premier ML, NLP, and software engineering conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, ICSE, FSE) - Accelerate innovation by working directly with customers to rapidly transition research breakthroughs into production systems About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

Great code assistants have to understand code the way an experienced engineer does, not as a wall of text but as a living structure of symbols, calls, types, and dependencies that span an entire repository. Most AI tools today flatten all of that into tokens and lose the structure that makes real software navigable. Amazon Web Services (AWS) is assembling an elite team of scientists and engineers to fix that, and we are hiring an Applied Scientist to build the intelligence layer that lets our AI development tools reason about code through its true structure. You will help create generative AI technologies, including Kiro IDE and CLI, that are transforming how software gets built. You will design how we turn the many signals around code, from static analysis and symbol graphs to build systems and language servers, into a single unified representation our models can reason over. This is a build-oriented research role where the questions are still open: how to fuse heterogeneous code signals, how to represent a whole repository so a model can find and use the right context, and how to ground an assistant's reasoning in the structure of real software rather than text alone. Your work will reach millions of developers, and you will own this direction end to end, from framing the approach to shipping it in a product engineers rely on every day. Key job responsibilities - Design a unified representation of code that fuses static analysis, symbol and call graphs, type information, and build system context - Build the methods and models that consume that representation to power code navigation, retrieval, and reasoning at repository scale - Invent and implement state-of-the-art machine learning solutions that operate at unprecedented Amazon scale - Deploy products that directly impact the daily workflows of millions of developers worldwide - Publish and present your work at premier ML, NLP, and software engineering conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, ICSE, FSE) - Accelerate innovation by working directly with customers to rapidly transition research breakthroughs into production systems About the team Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

We are seeking Data Scientist II with strong science application skills to join our Device Economics team. This role will focus primarily on Amazon's innovative devices and services (e.g. Echo Family of Devices), working at the intersection of economic modeling, forecasting science, and business strategy. The ideal candidate will be responsible for pre-launch forecasts, annualized overall forecasts, identifying substitution patterns, and partnering closely with product managers and marketing managers to understand the evolution of the Devices portfolio. Key job responsibilities Forecasting & Modeling 1. Develop and maintain pre-launch forecasts and annualized overall forecasts for Amazon Devices 2. Identify and model substitution patterns across the device portfolio 3. Build economic and financial models to support demand planning and business decisions 4. Formulate relevant analytical frameworks to address key economic issues in device forecasting Science Communication & Collaboration 1. Explain complex science models and methodologies to non-technical stakeholders including product managers and marketing managers 2. Collaborate with economists, data scientists, and applied scientists across Decision Science 3. Present results of analyses to cross-functional teams and leadership 4. Build trust in science models and forecast outputs with product teams Innovation & Strategic Thinking 1. Think creatively about ways that leading-edge analytics and emerging data sources can address Devices' most pressing business challenges 2. Help internal teams leverage analytic tools to better manage innovation 3. Conduct empirical studies and perform quantitative and qualitative research 4. Identify opportunities to improve forecasting accuracy and business impact Cross-Functional Partnership 1. Work closely with product managers and marketing managers to understand portfolio evolution and business strategy 2. Support DSO leadership in quarterly business reviews and strategic planning A day in the life Your days will be split between refining and building models and working with business leaders to interpret them. You own science-based forecasts that can directly impact Amazon's bottom line on the order of multi-million dollar decisions. - You will perform model refreshes or updates to analyses as needed; and, - You will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems. About the team The Decision Science team within DSO (Device Supply Organization) is responsible for forecasting and demand planning initiatives across Amazon Devices. The DSO team of 300+ engineers, scientists, and PMs applies quantitative methods and data-driven approaches to replace judgment-based decisions with science-driven forecasts. Decision Science focuses on lifetime demand forecasting using econometric and machine learning models for rapid reforecasting, mix adjustments, and portfolio management for new product launches. We also inform to go/no-go investment decision for new product initiatives

Have you ever wondered how Amazon predicts when your order will arrive and how we ensure that it actually arrives on at the promised date/time? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's logistics network including our planes, trucks, and vans sound exciting to you? If so, then we want to talk with you! At Amazon's Supply Chain Optimization Technologies (SCOT), we are tasked with optimizing the fulfilment on customer orders so that we fulfil all orders worldwide in the most intelligent manner while ensuring Amazon customers get their orders on time. Amazon Fulfillment Planning & Execution (FPX) Science team within SCOT- Fulfilment Optimization group is seeking a Senior Applied Scientist with expertise in Optimization and a proven record of solving business problems through scalable Optimization solutions. FPX Science tackles some of the most mathematically complex challenges in transportation planning and execution space to improve Amazon's operational efficiency worldwide. We own Amazon’s global fulfilment planning and execution. The team also owns the short and mid-term-term network planning and execution that determines the optimal flow of customer orders through Amazon fulfilment network. This includes developing sophisticated math models that assign orders to fulfilment centres to be picked and packed and then planning the optimal ship method in terms of cost, speed and carbon impact to deliver to the customer. These plans drive downstream decisions that are in the billions of dollars at Amazon Scale worldwide! The systems we build are entirely in-house, and are on the forefront of both academic and applied research in large scale supply chain planning, optimization, machine learning and statistics. These systems operate at various scales, from real-time decision system that completes thousands of transactions per seconds, to large scale distributed system that optimize Amazon’s fulfilment network. Your tech solution will have large impacts to the physical supply chain of Amazon, and play a key role in improving Amazon consumer business’s long-term profitability. If you are interested in diving into a multi-discipline, high impact space this is the team for you. We’re looking for a passionate, results-oriented, and inventive Senior Applied Scientist who can create and improve optimization models for our outbound transportation planning and execution systems. In addition, you will be working on design, development and evaluation of highly innovative Optimization models for solving complex business problems in the area of outbound transportation planning systems. Watch http://bit.ly/amazon-scot to get the big picture. Key job responsibilities As a Senior Applied Scientist, you will propose and deploy solutions that will likely draw from a range of scientific areas such as Optimization, machine learning, advanced statistical modeling, and graph models. You will be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry, with some of the best product managers, research scientists, statisticians, and software engineers to integrate scientific work into production systems. You will bring deep technical expertise in the area of Mathematical Optimization, and play an integral part in building Amazon's Fulfillment Optimization systems. Other responsibilities include: * Design, development and evaluation of highly innovative Math models for solving complex business problems. * Research and apply the latest Optimization techniques and best practices from both academia and industry. * Think about customers and how to improve the customer delivery experience. * Use and analytical techniques to create scalable solutions for business problems. * Work closely with software engineering teams to build model implementations and integrate successful models and algorithms in production systems at very large scale. * Technically lead and mentor other scientists in team. * Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. A day in the life This is a great role for someone who likes to learn new things. You will have the opportunity to learn all about how Amazon plans for and executes within its logistics network including Fulfillment Centres, Air Stations, Sort Centres, Delivery Stations, and more. You will need to meet with many internal customers to understand their business and their challenges as you develop an effective solution to enable us to collectively scale. Our leadership is very interested in this effort so you will find lots of opportunities to get your ideas and plans in front of them for evaluation and alignment. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready mathematical optimization solutions. A successful candidate is able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career. About the team Fulfillment Planning & Execution Science team contains a group of scientists with different technical backgrounds including Operations Research and Machine Learning who will collaborate closely with you on your projects. Our team directly supports critical functional areas across Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We tackle some of the most mathematically complex challenges in facility and transportation planning and execution to improve Amazon's operational efficiency worldwide and at a scale that is unique to Amazon. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges. We disambiguate complex supply chain problems and create solutions to solve those problems at scale.

AWS Billing requires a Scientist with a proven track record in time-series based anomaly detection. During the billing process there are many unknown-unknowns and detecting these are vital for ensuring an accurate bill. Key job responsibilities You will work closely with the usage metering and bill computation teams to build anomaly detectors that can find issues in usage and invoices. You will build production models that will be the cornerstone of our efforts to ensure accurate bills. About the team We are a small science team that work on high-impact projects in AWS Billing. Our work is foundational for Billing's accuracy goals. We publish our results in top conferences and play an active role in academia.

The Amazon Smart Vehicles (ASV) science team is seeking a passionate and skilled Applied Scientist with extensive expertise in advanced LLM technologies. This role involves innovating in rapidly evolving areas of AI research, focusing on creating personalized services to enhance drivers' and passengers' experiences. Your work will aim to simplify their lives, keep them informed, entertained, productive, and safe on the road, with direct application to prominent Amazon products. If you have extensive expertise in LLMs, natural language processing, and machine learning, along with experience in high-performing research teams, this could be the perfect opportunity for you. Our dynamic and fast-paced environment demands a high level of independence in decision-making and the ability to drive ambitious research initiatives through to production. You will collaborate closely with other science and engineering teams, as well as business stakeholders, to ensure your contributions are both impactful and delivered with maximum efficiency. Key job responsibilities - Leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI) - Work with talented peers to lead the development of novel algorithms and modeling techniques to advance the state of the art with LLMs - Collaborate with other science and engineering teams as well as business stakeholders to maximize the velocity and impact of your contributions About the team This is an exciting moment to lead in AI research and application. As part of the Amazon Smart Vehicles science team, you have the opportunity to shape the future by enhancing information-driven experiences for Amazon customers around the globe. Your work will directly influence customers through innovative products and services powered by language and multimodal technology!

Join us in the evolution of Amazon’s Seller business! The Selling Partner Selection Success organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP selection growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources. We are looking for a Applied Scientist II to work on our growth agent vision on seller recommendation to improve our SP growth strategy and drive new seller success. As a successful Applied Scientist on our talented team of applied scientists and economists, you will leverage the latest scientific methods, including statistics, machine learning, deep learning and GenAI technology to solve complex problems, and collaborate with engineering, research, and business teams to deliver optimized experience (AI infused products, agentic experiences) on behalf of our sellers. You need to have deep understanding on the business domain and have the ability to connect business with science. You are also strong in AI/ML/GenAI technology and scientific foundation with the ability to collaborate with engineering to put models in production to answer specific business questions. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research). Key job responsibilities As an Applied Scientist II in the team, you will: - Identify opportunities to improve SP growth and translate those opportunities into science problems via principled GenAI solutions . - Design and execute roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers. - Work with our engineering partners and draw upon your experience to meet latency and other system constraints. - Be responsible for communicating our science innovations to the broader internal & external scientific community.