Careers

Device Economics is looking for a senior economist experienced in causal inference, machine learning, empirical industrial organization, and scaled systems to work on business problems to advance critical resource allocation and pricing decisions in the Amazon Devices org. Senior roles lead vision setting, methods innovation, and act as thought leaders to Devices finance and business executives. Output will be included in scaled systems to automate existing processes and to maximize business and customer objectives. Amazon Devices designs and builds Amazon first-party consumer electronics products to delight and engage customers. Amazon Devices represents a highly complex space with 100+ products across several product categories (e-readers [Kindle], tablets [Fire Tablets], smart speakers and audio assistants [Echo], wifi routers [eero], and video doorbells and cameras [Ring and Blink]), for sale both online and in offline retailers in several regions. The space becomes more complex with dynamic product offering with new product launches and new marketplace launches. The Device Economics team leads in analyzing these complex marketplace dynamics to enable science-driven decision making in the Devices org. Device Economics achieves this through scientific applications that provide deep understanding of customer preferences. Our team’s outputs inform product development decisions, investments in future product categories, and product pricing and promotion. We have achieved substantial impact on the Devices business, and will achieve more. Device Economics seeks an experienced economist adept in measuring customer preferences and behaviors with proven capacity to innovate, scale measurement, drive rigor, and mentor talent. Key job responsibilities The candidate will work with Amazon Devices science leadership to refine science roadmaps, models, and priorities for innovation and simplification, and advance adoption of insights to influence important resource allocation and prioritization decisions. Effective communication skills (verbal and written) are required to ensure success of this collaboration. The candidate must be passionate about advancing science for business and customer impact.

Amazon Advertising operates at the intersection of eCommerce and advertising, offering a rich array of advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers reach Amazon customers on Amazon owned and operated sites, other high quality sites across the web, and on millions of TV, tablet, and mobile devices. We start with the customer and work backwards in everything we do, including advertising. If you're interested in working in a world-class organization with a relentless focus on the customer, you've come to the right place! Our team within the CreativeX organization drives engaging user experiences through content and interactivity enrichment, enabling advertisers to reach target audiences at scale. We are looking for a passionate, talented, and resourceful Senior Applied Scientist in the field of Personalization and Recommender Systems to invent and build scalable solutions for more engaging and customized ads for brands of all sizes across the marketing funnel. Key job responsibilities The scientist will lead research and development for streaming TV dynamic creative optimization and content personalization to drive measurable improvements in user engagement and advertising performance. Partner with cross-functional engineering, product, and business teams to execute our product vision, design and implement scalable machine learning solutions that leverage generative AI capabilities, drive innovation in AI-powered content optimization and audience engagement, and demonstrate strong technical leadership, stakeholder management, and project execution skills.

AWS Central Economics and Science is looking for an applied optimization scientist to join the Capacity Economics team. This scientist will work at the intersection of economics and optimization, contributing to projects that span both the Capacity Economics team and the EC2 Optimization Science team within the EC2 Capacity Org. The Capacity Economics team is a small, applied-solutions-focused group of economists and scientists focused on lowering AWS' cost-to-serve through science-based models, analyses, and insights. Our work spans pricing design, capacity allocation, and infrastructure economics—problems that require both rigorous quantitative methods and a deep understanding of how markets and incentives shape outcomes. The candidate will work on a portfolio of projects that include optimizing internal prices for compute resources and working across Finance, Engineering, and Capacity stakeholders to design mechanisms that align incentives and reduce costs. The role will also engage with the infrastructure organization, including Amazon Cloud Logistics, to support the efficient transit and deployment of server rack builds. Key job responsibilities On the EC2 Optimization Science (EC2-OptSci) side, the candidate will contribute to the design, implementation, and scaling of decision-making algorithms that manage EC2's virtual and physical capacity systems. EC2 Capacity owns EC2's top-level customer satisfaction metric—capacity availability—and the forecasting and decision-making systems that drive significant CapEx investments in server ordering for AWS data centers. Optimization Science is a core team involved in the end-to-end design and implementation of decision-making systems that manage the trade-off between CapEx and capacity availability while matching demand and supply at different planning horizons. The candidate will participate in science and engineering reviews with the Optimization Science team and will be expected to contribute to the rigor and quality of that team's technical work. In a typical optimization science project, we analyze large volumes of data and develop prescriptive optimization models with inputs from ML or statistical models and business users. Solution approaches are validated through simulations and/or production A/B tests. Success requires scientific breadth to understand the interactions between different phases of a project—from data analysis through to production—including resolving issues after rollout. As an Applied Scientist working on an optimization project, you will be hands-on with mathematical modeling and implementation and will contribute to the design of engineering systems with scalability, extensibility, maintainability, and correctness in mind. You will review approaches by other scientists and engineers in terms of business relevance, technical validity, engineering/science interface, and computational performance. Communicating results to guide business direction and working with software development teams to implement ideas in code is key to success. You will write technical and business documents that influence engineering investments and business direction. Collaborating with scientists, software engineers, economists, and product managers, you will develop creative, novel, and data-driven approaches to improve cloud compute offerings and impact the bottom line of AWS. A day in the life The mission of the ACES Capacity Economics Team is to provide AWS Finance and AWS Service teams with economic frameworks, statistical models, and system-design insights to understand and optimize AWS’ cost structure. Internally, we succinctly boil our objective down to this: to help AWS to serve its total demand at a lower cost. 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.

Economists on the Seller Fee Science team design scalable strategies and technology for the fees Amazon charges to third-party sellers, world wide. Our challenge is to facilitate continued improvement in both customer experience (selection, prices, delivery speed) and selling partner growth and innovation, while optimizing for Amazon's long-term profits. Key job responsibilities Design and develop models to assess the causal impact of fees, and fee-related policy on third party sellers’ behavior and business performance. Lead enhancements into existing fee calculation models to maximize the long term health of the Amazon third-party marketplace. Own the scientific vision and direction related to fees worldwide. Collaborate with product managers, data scientists, and software developers to incorporate models into production processes and influence senior leaders. Act as an ambassador of our team in the broader scientific community.

Are you a MS or PhD student interested in a 2024 Applied Science Internship in the fields of Speech, Robotics, Computer Vision, or Machine Learning/Deep Learning? Do you enjoy diving deep into hard technical problems and coming up with solutions that enable successful products that improve the lives of people in a meaningful way? If this describes you, come join our research teams at Amazon. Key job responsibilities As an Applied Scientist, you will have access to large datasets with billions of images and video to build large-scale machine learning systems. Additionally, you will analyze and model terabytes of text, images, and other types of data to solve real-world problems and translate business and functional requirements into quick prototypes or proofs of concept. We are looking for smart scientists capable of using a variety of domain expertise combined with machine learning and statistical techniques to invent, design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

职位：Applied scientist 应用科学家实习生 毕业时间：2026年10月 - 2027年7月之间毕业的应届毕业生 · 入职日期：2026年6月及之前 · 实习时间：保证一周实习4-5天全职实习，至少持续3个月 · 工作地点：深圳福田区 投递须知： 1 填写简历申请时，请把必填和非必填项都填写完整。提交简历之后就无法修改了哦！ 2 学校的英文全称请准确填写。中英文对应表请查这里（无法浏览请登录后浏览）https://docs.qq.com/sheet/DVmdaa1BCV0RBbnlR?tab=BB08J2 关于职位 Amazon Device &Services Asia团队正在寻找一位充满好奇心、善于沟通的应用科学家实习生，成为连接前沿AI研究与现实世界认知的桥梁。这是一个独特的角色——既需要动手参与机器学习项目，又要接受将复杂AI概念转化为通俗易懂内容的创意挑战。D&S Asia是亚马逊设备与服务业务在亚洲的支柱组织，自2009年支持Kindle制造起步，现已发展为横跨软硬件、AI（Alexa）及智能家居（Ring/Blink）的综合性团队，持续驱动区域业务创新与人才发展。 你将做什么 • 解密AI： 将复杂的技术发现转化为直观的解释、博客文章、教程或互动演示，让非技术背景的业务方和更广泛的社区都能理解 • 技术叙事： 与工程团队协作，以清晰、引人入胜的方式记录AI的能力与局限性 • 知识共享： 协助开发内部工作坊或"AI入门"课程，提升跨职能团队（产品、设计、商务）的AI素养 • 保持前沿： 持续学习并整合最新突破（如大语言模型、扩散模型、智能体），为团队输出简明易懂的趋势简报 • 研究与应用： 参与端到端的应用研究项目，从文献综述到原型开发，涵盖自然语言处理、计算机视觉或多模态AI领域

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the International Emerging Stores organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team Central Machine Learning team works closely with the IES business and engineering teams in building ML solutions that create an impact for Emerging Marketplaces. This is a great opportunity to leverage your machine learning and data mining skills to create a direct impact on millions of consumers and end users.

Passionate about books? The Amazon Books team is looking for a talented Applied Scientist II to help invent, design, and deliver science solutions to make it easier for millions of customers to find the next book they will love. In this role, you will - Be a part of a growing team of scientists, economists, engineers, analysts, and business partners. - Use Amazon’s large-scale computing and data resources to generate deep understandings of our customers and products. - Build highly accurate models (and/or agentic systems) to enhance the book reading & discovery experiences. - Design, implement, and deliver novel solutions to some of Amazon’s oldest problems. Key job responsibilities - Inspect science initiatives across Amazon to identify opportunities for application and scaling within book reading and discovery experiences. - Participate in team design, scoping, and prioritization discussions while mapping business goals to scientific problems and aligning business metrics with technical metrics. - Spearhead the design and implementation of new features through thorough research and collaboration with cross-functional teams. - Initiate the design, development, execution, and implementation of project components with input and guidance from team members. - Work with Software Development Engineers (SDEs) to deliver production-ready solutions that benefit customers and business operations. - Invent, refine, and develop solutions to ensure they meet customer needs and team objectives. - Demonstrate ability to use reasonable assumptions, data analysis, and customer requirements to solve complex problems. - Write secure, stable, testable, and maintainable code with minimal defects while taking full responsibility for your components. - Possess strong understanding of data structures, algorithms, model evaluation techniques, performance optimization, and trade-off analysis. - Follow engineering and scientific method best practices, including design reviews, model validation, and comprehensive testing. - Maintain current knowledge of research trends in your field and apply rigorous scrutiny to results and methodologies. A day in the life In this role, you will address complex Books customer challenges by developing innovative solutions that leverage the advancements in science. Working alongside a talented team of scientists, you will conduct research and execute experiments designed to enhance the Books reading and shopping experience. Your responsibilities will encompass close collaboration with cross-functional partner teams, including engineering, product management, and fellow scientists, to ensure optimal data quality, robust model development, and successful productionization of scientific solutions. Additionally, you will provide mentorship to other scientists, conduct reviews of their work, and contribute to the development of team roadmaps. About the team The team consists of a collaborative group of scientists, product leaders, and dedicated engineering teams. We work with multiple partner teams to leverage our systems to drive a diverse array of customer experiences, owned both by ourselves and others, that enable shoppers to easily find their perfect next read and enable delightful reading experiences that would make Kindle the best place to read.

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value.

Take Earth's most customer-centric company. Mix in hundreds of millions of shoppers spending tens of billions of dollars annually, an exciting opportunity to build next-generation shopping experiences, Amazon’s tremendous computational resources, and our extensive e-Commerce experience. What do you get? The most exciting position in the industry. About our organization: Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, big data, distributed systems, and user experience design to deliver the best shopping experiences for our customers. We run global experiments and our work has revolutionized e-commerce with features such as "Keep shopping for", "Tap to explore", “Customers who bought this item also bought”, and “Frequently bought together” among others. Amazon’s internal surveys regularly recognize us as one of the best engineering organizations to work for in the company, with visible high-impact work, low operational load, respectful work-life balance, and continual opportunity to learn and grow. You will play a critical role in ideation for the team. We are building the next generation ML systems that powers the biggest shopping engine on earth, and we hope you will join us! Key job responsibilities As an Applied Scientist on the team you will be working on cutting edge ways to help customers find the right products and content on their shopping journey. Our goal is to help customers achieve their objective seamlessly while shopping on Amazon. We are investing in multiple fronts including but not limited to GenerativeAI, LLMs, transformers, sequence models, reinforcement learning, MABs. This is an opportunity to come in on Day0 and influence the science roadmap of one of the most interesting problem spaces at Amazon - understanding the Amazon customer to build deeply personalized and adaptive shopping experiences. We will be working on applying cutting edge science and research into production to elevate the customer experience. You will be part of a multidisciplinary team, working on one of the largest scale machine learning systems in the company. You will hone your skills in areas such as deep learning and reinforcement learning while building scalable industrial systems. As a member of a highly leveraged team of talented engineers and ML scientists, you will have a unique opportunity to help build infrastructure that accesses petabytes of data to produce and deliver models that deliver state of the art customer experiences. About the team Our mission is to delight every Amazon customer with a consistent and adaptive personalized shopping experience. We achieve our mission through investments in large scale machine learning, distributed systems and user experience with the purpose of delivering the future of shopping on Amazon. We are seeking an Applied Scientist to work on step function science improvements to help achieve SOTA results and to help build new Personalization experiences for Amazon customers.