Careers

Amazon.com strives to be Earth's most customer-centric company where people can find and discover anything they want to buy online. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Are you seeking an environment where you can drive innovation? Do you want to apply learning techniques and advanced mathematical modeling to solve real world problems? Do you want to play a key role in the future of Amazon's Retail business? This job for you! The Customer Behavior Analytics (CBA) team at Amazon is responsible for the architecture, design, implementation of tools used to understand customer behavior and value generation for all Amazon programs. Come and join us! Amazon’s CBA team is looking for Economists, who can work at the intersection of economics, statistics and machine learning; and leverage the power of big data to solve complex problems like long-term causal effect estimation. Key job responsibilities Economists at Amazon are expected to work directly with other Economists and senior management on key business problems in retail, international retail, cloud computing, third party merchants, search, Kindle, streaming video, and operations. Amazon economists will apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. You will build econometric models, using our world class data systems, and apply economic theory to solve business problems in a fast moving environment. Economists at Amazon will be expected to develop new techniques to process large data sets, address quantitative problems, and contribute to design of automated systems around the company. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA

Inventory Planning and Control (IPC) is seeking a Principal Applied Scientist to join its SPCB research team to help shape how Amazon supply chain optimizes inventory decisions in its global multi-tiered fulfillment network. SPCB research team owns the core decision models in the space of S&OP Planning, Placement, Capacity Control and Buying. Our models decide when, where, and how much we should buy, flow, and hold inventories in our global fulfillment network to meet Amazon’s business goals and to make our customers happy. We do this for hundreds of millions of items and hundreds of product lines worth billions of dollars of inventory world-wide for both our Retail and Seller business. Our systems are built entirely in-house, for which we constantly develop new technologies in automated inventory planning, prediction, optimization and simulation. Our systems operate at various scales, from real-time decision system that completes thousands of transactions per seconds, to large scale distributed system that optimizes the inventory decisions over millions of products simultaneously. 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. You will work on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. Key job responsibilities As a Principal Applied Scientist in IPC, you will partner with the senior tech leaders in the organization to define the long term architecture of our decision optimization and prediction systems. You will play a key role in developing long term strategic solutions that have business impact beyond the scope of the organization. You will bring technical expertise in several technical areas of Operations Research or Machine Learning, and are able to help team to overcome key technical blockers. You will ensure senior leaders in the organization are up to speed on important trends, tools and technologies and how they will be used to impact the business. You are 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. You will analyze large amounts of business data, define new metrics and business cases, design simulations and experiments, develop scientific, and collaborate with teammates in business, software, and research. 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. A day in the life S&OP Planning, Placement, Capacity Control and Buying (SPCB) space is at the center of Amazon’s supply chain. In this role, you will have opportunity to work with partners and stakeholders from Amazon’s retail, seller and operation departments worldwide. You will understand their challenges and pain points, and help develop solutions that improve how Amazon manages inventory in our global fulfillment network. To implement your solutions, you will work closely with our in-house product and engineering teams. Your work will have high visibility and impacts to Amazon’s business operation. About the team SPCB research team contains a large group of scientists with different technical backgrounds, who will collaborate closely with you on your projects. Our team directly supports 8 functional areas and the research needs of the corresponding engineering and product teams. We promote experimentation and learn by building. Our team constantly tackles some of the hardest modelling, optimization and prediction problems in inventory planning, optimization and control 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 are open to hiring candidates to work out of one of the following locations: New York City, NY, USA | New York, NY, USA

Amazon Fulfillment Optimization, Network Planning and Fulfillment Execution Science group is seeking a Principal Applied Scientist with expertise in Machine Learning and a proven record of solving business problems through scalable ML solutions. Network Planning and Execution tackles some of the most mathematically complex challenges in facility and transportation planning to improve Amazon's operational efficiency worldwide. We own Amazon’s global fulfillment center and transportation topology planning and execution. The team also owns the short-term network planning that determines the optimal flow of customer orders through Amazon fulfillment network. This includes developing sophisticated math models and controllers that assign orders to fulfillment centers 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. The systems we build are entirely in-house, and are on the cutting edge 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. As Amazon continues to build and expand the first party delivery network, this role will be critical to realize this vision. 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. Key job responsibilities As a Principal Applied Scientist within Network Planning and Execution team, you will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised, semi-supervised and unsupervised learning, reinforcement learning, advanced statistical modeling, and graph models. You will have an opportunity to 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 partner with the senior tech leaders in the organization to define the long-term vision of our network planning and execution systems. You will play a key role in developing long term strategic solutions that have business impact beyond the scope of the organization. You will bring deep technical expertise in the area of Machine Learning, and will play an integral part in building Amazon's Fulfillment Optimization systems. Other responsibilities include: • Research and develop machine learning models to solve diverse business problems faced within Network Planning and Execution team. • Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. • Review and audit modeling processes and results for other scientists, both junior and senior. • Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers • You will ensure senior leaders in the organization are up to speed on important trends, tools and technologies and how they will be used to impact the business. A day in the life In this role, you will be a technical leader in machine learning with significant scope, impact, and high visibility. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. As a Principal Applied Scientist on the team, you will be involved in every aspect of the process - from ideation, business analysis and scientific research, through to development and deployment of advanced models - giving you a real sense of ownership. From day one, you will be working with bar raising scientists, engineers, and designers. You are expected to make decisions about technology, models and methodology choices. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work and mentor engineers and other scientists. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready machine learning 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 Optimization, Network Planning and Fulfillment Execution Science team contains a group of scientists with different technical backgrounds including Machine Learning and Operations Research, who will collaborate closely with you on your projects. Our team directly supports multiple 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 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 ML and optimization solutions to solve those problems at scale. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA

The Fulfillment by Amazon (FBA) team is looking for a passionate, curious, and creative Principal Applied Scientist, with expertise in machine learning and a proven record of solving business problems through scalable ML solutions, to join our top-notch cross-domain FBA science team. We want to learn seller behavior, understand seller experience, build automated LLM-based assistant to sellers or an indefinitely patient, infinitely knowledgeable co-pilot, recommend right actions to sellers, design seller policies and incentives, and develop science products and services that empower third-party sellers to grow their businesses. We also predict potentially costly defects that may occur during packing, shipping, receiving and storing the inventory. We aim to prevent such defects before occurring while we are also fulfilling customer demand as quickly and efficiently as possible, in addition to managing returns and reimbursements. To do so, we build and innovate science solutions at the intersection of machine learning, statistics, economics, operations research, and data analytics. As a principal applied scientist, you will propose and deploy solutions that will likely draw from a range of scientific areas such as supervised and unsupervised learning, recommendation systems, advanced statistical modeling, LLMs, transfer learning, and reinforcement learning. This role has high visibility to senior Amazon business leaders and involves working with other scientists, and partnering with engineering and product teams to integrate scientific work into production systems. Key job responsibilities As a senior member of the science team, you will play an integral part in building Amazon's FBA management system with the following technical and leadership responsibilities: • Research and develop automated LLM-based assistant to Sellers - an indefinitely patient, infinitely knowledgeable co-pilot that present growth opportunities, cost-out recommendations to maximize business outcomes for Sellers. • Research and develop machine learning models to solve diverse business problems faced in Seller inventory management systems. • Define a long-term science vision and roadmap for the team, driven fundamentally from our customers' needs, translating those directions into specific plans for research and applied scientists, as well as engineering and product teams. • Drive and execute machine learning projects/products end-to-end: from ideation, analysis, prototyping, development, metrics, and monitoring. • Review and audit modeling processes and results for other scientists, both junior and senior. • Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers A day in the life In this role, you will be a technical leader in machine learning with significant scope, impact, and high visibility. Your solutions may lead to billions of dollars impact on either the topline or the bottom line of Amazon third-party seller business. 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 models - giving you a real sense of ownership. From day one, you will be working with experienced scientists, engineers, and designers who love what they do. You are expected to make decisions about technology, models and methodology choices. You will strive for simplicity, and demonstrate judgment backed by mathematical proof. You will also collaborate with the broader decision and research science community in Amazon to broaden the horizon of your work and mentor engineers and scientists. The successful candidate will have the strong expertise in applying machine learning models in an applied environment and is looking for her/his next opportunity to innovate, build, deliver, and impress. We are seeking someone who wants to lead projects that require innovative thinking and deep technical problem-solving skills to create production-ready machine learning solutions. The candidate will need to be entrepreneurial, wear many hats, and work in a fast-paced, high-energy, highly collaborative environment. We value highly technical people who know their subject matter deeply and are willing to learn new areas. 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 by Amazon (FBA) is a service that allows sellers to outsource order fulfillment to Amazon, allowing sellers to leverage Amazon’s world-class facilities to provide customers Prime delivery promise. Sellers gain access to Prime members worldwide, see their sales lift, and are free to focus their time and resources on what they do best while Amazon manages fulfillment. Over the last several years, sellers have enjoyed strong business growth with FBA shipping more than half of all products offered by Amazon. FBA focuses on helping sellers with automating and optimizing the third-party supply chain. FBA sellers leverage Amazon’s expertise in machine learning, optimization, data analytics, econometrics, and market design to deliver the best inventory management experience to sellers. We work full-stack, from foundational backend systems to future-forward user interfaces. Our culture is centered on rapid prototyping, rigorous experimentation, and data-driven decision-making. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA

Project Kuiper is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. We are looking for an experienced Principal Data Scientist to architect cutting edge test infrastructure and lead the development of data models and analysis tools to represent the ground truth about satellite test results in order to facilitate important business decisions. Our team is responsible for core infrastructure and tools that will serve as the backbone of automated satellite testing operations to enable rapid scaling of manufacturing processes. As a Project Kuiper Data Scientist you will own the architecture definition and development of data analysis tools to aid engineering and production teams in deciding flight-worthiness of each Kuiper satellite and historical traceability tools to enable simplified discovery and interpretation of past test data. You will work with multiple engineering, software and manufacturing teams across ground and space systems, to specify requirements, define data collection, interpretation strategies, data pipelines and implement data analysis and reporting tools for Integrated Vehicle tests. Your focus will be in optimizing the analysis of test results to enable Project Kuiper production plans. Key job responsibilities • Understanding drivers, impacts, and key influences on Integrated Vehicle test results. • Lead the design, build and implementation of production systems that take inputs from multiple models and make decisions in real time for Integrated Vehicle test results. • Drive actions at scale to optimize test methodology and drive increases to satellite reliability. • Automate feedback loops for test result algorithms in production. • Leverage Amazon systems and tools to effectively work with, process and analyze terabytes of data. About the team The Automated Vehicle Testing Team is responsible for core infrastructure and tools that will serve as the backbone of automated satellite testing operations to enable rapid scaling of manufacturing processes. We are open to hiring candidates to work out of one of the following locations: Redmond, WA, USA

深度学习技术的进步正在推动人工智能领域的快速发展，将多个学科领域如计算机视觉、自然语言处理、图与网络数据理解、系统工程以及优化理论等紧密结合。同时，众多深度学习开源项目，不仅加速了学术研究的进程，也推动了技术的商业化和社会化应用。 亚马逊上海人工智能研究院是深度学习研究的领军团队之一。我们的研究包括但不限于以下方向：深度学习基础理论、自然语言处理、计算机视觉、图机器学习、高性能计算、智能推荐系统、反欺诈与风控、知识图谱构建以及智能决策系统等。研究院积极推进深度学习的开源生态建设，其主攻的深度图网络DGL（Deep Graph Library）开源库是该领域的领跑平台。我们致力于通过跨领域的研究和合作，推进人工智能技术的边界，并通过开源项目的贡献，促进全球研究社区的共同进步。 我们诚邀对深度学习和人工智能充满热情的实习生加入我们的团队。我们希望通过你的智慧和努力，共同探索深度学习在各研究方向和应用领域的新算法和模型，进一步扩大我们研究的深度和广度。在实习期间，你将有机会在资深mentor的指导下深入研究深度学习的子领域或具体应用场景，掌握该领域的发展趋势和关键技术，实现创新模型，并有可能通过开源贡献或学术发表，向全球科研和工业界展示你的成果。 除了每天能与亚马逊上海人工智能研究院的同事们交流外，实习生还将有机会和亚马逊其他部门的同事、上海一流高校的顶级教授、和来自世界各地的一流专家合作，如Alon Halevy, Christos Faloutsos、Stefano Soatto、Pietro Perona、George Karypis、Thomas Brox、 David Wipf、付彦伟、张伟楠、张牧涵、邱锡鹏、张岳、张峥等。 点击下方链接查看申请手册获得更多信息： https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN

深度学习是当前人工智能领域最热门的研究方向，它将机器学习、统计学、优化和系统工程紧密地结合在一起。深度学习成功的关键因素之一便是在计算机视觉、自然语言处理、时间序列、深度图学习和强化学习等领域里出现的众多开源项目。这些项目既可以被用来复现研究成果，也可以帮助实际应用的快速部署落地。 亚马逊上海人工智能研究院主攻的深度图网络DGL（Deep Graph Library）开源库是该领域的领跑平台。基于深度图计算的研究内容非常丰富，涵盖：1）基础理论研究；2）高性能、高容量核心引擎开发；3）重要的子领域模型研发（推荐系统、反欺诈和风控、知识图谱、制药、时域深度图计算、计算机视觉、自然语言处理、自动知识抽取）；4）客户的应用场景落地。 我们正在招募聪明努力的实习生，希望一起为开源生态添砖加瓦。我们期望能实现更多图神经网络在各个研究方向与应用领域的高级算法，让DGL在前沿研究与应用落地上越来越强大称手。在实习期间，你将在mentor的指导下深入调研图神经网络的具体子研究或应用领域，了解该领域发展脉络与重要工作，实现重要的模型快速进入该领域的最前沿，在此基础上提出更新的算法，通过开源发布让全世界的科研人员受益，也通过合作发表论文分享给整个学术圈。 除了每天能与亚马逊上海人工智能研究院的同事们交流外，实习生还将有机会和亚马逊其他部门的同事、上海一流高校的顶级教授、和来自世界各地的一流专家合作，如Matthias Bethge、Stefano Soatto、Pietro Perona、George Karypis、Thomas Brox、David Wipf、付彦伟、张伟楠、邱锡鹏、张岳、张峥等。 点击下方链接查看申请手册获得更多信息： https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e Key job responsibilities 使用Python/C++ 开发并维护广受欢迎的开源机器学习系统Deep Graph Library (DGL) We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN

深度学习技术的进步正在推动人工智能领域的快速发展，将多个学科领域如计算机视觉、自然语言处理、图与网络数据理解、系统工程以及优化理论等紧密结合。同时，众多深度学习开源项目，不仅加速了学术研究的进程，也推动了技术的商业化和社会化应用。 亚马逊上海人工智能研究院是深度学习研究的领军团队之一。我们的研究包括但不限于以下方向：深度学习基础理论、自然语言处理、计算机视觉、图机器学习、高性能计算、智能推荐系统、反欺诈与风控、知识图谱构建以及智能决策系统等。研究院积极推进深度学习的开源生态建设，其主攻的深度图网络DGL（Deep Graph Library）开源库是该领域的领跑平台。我们致力于通过跨领域的研究和合作，推进人工智能技术的边界，并通过开源项目的贡献，促进全球研究社区的共同进步。 我们诚邀对深度学习和人工智能充满热情的实习生加入我们的团队。我们希望通过你的智慧和努力，共同探索深度学习在各研究方向和应用领域的新算法和模型，进一步扩大我们研究的深度和广度。在实习期间，你将有机会在资深mentor的指导下深入研究深度学习的子领域或具体应用场景，掌握该领域的发展趋势和关键技术，实现创新模型，并有可能通过开源贡献或学术发表，向全球科研和工业界展示你的成果。 除了每天能与亚马逊上海人工智能研究院的同事们交流外，实习生还将有机会和亚马逊其他部门的同事、上海一流高校的顶级教授、和来自世界各地的一流专家合作，如Alon Halevy, Christos Faloutsos、Stefano Soatto、Pietro Perona、George Karypis、Thomas Brox、 David Wipf、付彦伟、张伟楠、张牧涵、邱锡鹏、张岳、张峥等。 点击下方链接查看申请手册获得更多信息： https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN

深度学习技术的进步正在推动人工智能领域的快速发展，将多个学科领域如计算机视觉、自然语言处理、图与网络数据理解、系统工程以及优化理论等紧密结合。同时，众多深度学习开源项目，不仅加速了学术研究的进程，也推动了技术的商业化和社会化应用。 亚马逊上海人工智能研究院是深度学习研究的领军团队之一。我们的研究包括但不限于以下方向：深度学习基础理论、自然语言处理、计算机视觉、图机器学习、高性能计算、智能推荐系统、反欺诈与风控、知识图谱构建以及智能决策系统等。研究院积极推进深度学习的开源生态建设，其主攻的深度图网络DGL（Deep Graph Library）开源库是该领域的领跑平台。我们致力于通过跨领域的研究和合作，推进人工智能技术的边界，并通过开源项目的贡献，促进全球研究社区的共同进步。 我们诚邀对深度学习和人工智能充满热情的实习生加入我们的团队。我们希望通过你的智慧和努力，共同探索深度学习在各研究方向和应用领域的新算法和模型，进一步扩大我们研究的深度和广度。在实习期间，你将有机会在资深mentor的指导下深入研究深度学习的子领域或具体应用场景，掌握该领域的发展趋势和关键技术，实现创新模型，并有可能通过开源贡献或学术发表，向全球科研和工业界展示你的成果。 除了每天能与亚马逊上海人工智能研究院的同事们交流外，实习生还将有机会和亚马逊其他部门的同事、上海一流高校的顶级教授、和来自世界各地的一流专家合作，如Alon Halevy, Christos Faloutsos、Stefano Soatto、Pietro Perona、George Karypis、Thomas Brox、 David Wipf、付彦伟、张伟楠、张牧涵、邱锡鹏、张岳、张峥等。 The advancements in deep learning technology are driving rapid development in the field of artificial intelligence, closely integrating multiple disciplines such as computer vision, natural language processing, graph and network processing, systems engineering, and optimization theory. Moreover, numerous deep learning open-source projects have not only accelerated the pace of academic research but also promoted the commercialization and social application of technology. The AWS Shanghai AI Lab is one of the leading institutes in deep learning research. Our research areas include, but are not limited to, the foundational theory of deep learning, natural language processing, computer vision, graph machine learning, high-performance computing, intelligent recommendation systems, fraud detection and risk control, knowledge graph construction, and intelligent decision-making systems. The institute actively promotes the construction of an open-source ecosystem for deep learning, highlighting the Deep Graph Library (DGL) as a leading platform in its field. We are committed to pushing the boundaries of AI technology through interdisciplinary research and collaboration, and by contributing to open-source projects, we aim to promote progress with the global research community altogether. We warmly invite interns who are passionate about deep learning and artificial intelligence to join our team. We hope to explore new algorithms and models in various research directions and application areas of deep learning together, further expanding the depth and breadth of our research. During the internship, you will have the opportunity to delve into subfields or specific application scenarios of deep learning under the guidance of experienced mentors, grasp the development trends and key technologies of the field, create innovative models, and showcase your results to the global scientific and industrial communities through open-source contributions or academic publications. In addition to daily interactions with colleagues at the AWS Shanghai AI Lab, interns will also have the opportunity to collaborate with colleagues from other departments of Amazon, top professors from leading universities in Shanghai, and world-class experts from around the globe, such as Alon Halevy, Christos Faloutsos, Stefano Soatto, Pietro Perona, George Karypis, Thomas Brox, David Wipf, Yanwei Fu, Weinan Zhang, Muhan Zhang, Xipeng Qiu, Yue Zhang, Zheng Zhang, and others. 点击下方链接查看申请手册获得更多信息： https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN

Amazon is looking for talented Postdoctoral Scientists to join our global Science teams for a one-year, full-time research position. Postdoctoral Scientists will innovate as members of Amazon’s key global Science teams. Postdoctoral Scientists will join one of our global science teams focused on solving research-intense business problems by exploring new research ideas, accelerating scientific innovation and impact, and publishing their work in peer-reviewed scientific venues. Postdocs will raise the scientific bar across Amazon by diving deep into exploratory areas of research to enhance the customer experience and improve efficiencies. Key job responsibilities * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise. We are open to hiring candidates to work out of one of the following locations: Boston, MA, USA | Los Angeles, CA, USA | Nashville, TN, USA | New York, NY, USA | San Francisco, CA, USA | Seattle, WA, USA | Simi Valley, CA, USA | West Hollywood, CA, USA