Careers

The People eXperience and Technology Central Science (PXTCS) team uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. The Benefits Science team is looking for an economist to transform complex business challenges into actionable scientific insights. In this role, you will partner directly with business leaders to design and evaluate pilots, build models using large-scale data, and scale successful prototypes into company-wide policies and programs. We're looking for someone who can combine rigorous scientific thinking with practical business acumen and is passionate about using economics to improve employee experiences at scale. The ideal candidate will thrive in interdisciplinary environments, working alongside engineers, data scientists, and business leaders from diverse backgrounds. Key job responsibilities - Design and conduct rigorous evaluations of benefits programs - Support the development and application of structural models - Develop experiments to evaluate the impact of benefits initiatives - Communicate complex findings to business stakeholders in clear, actionable terms - Work with engineering teams to develop scalable tools that automate and streamline evaluation processes A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions.

Are you passionate about revolutionizing the Architecture, Engineering, Construction, and Ownership (AECO) industry through next-generation artificial intelligence? AWS Data Center Design Engineering is seeking an exceptional Applied Scientist with Building Information Modeling (BIM) domain expertise to transform how we design and deliver Amazon's global data center infrastructure. In this role, you will build state-of-the-art machine learning systems that accelerate data center design workflows by automating building design tasks in BIM environments and ensuring compliance with building codes and design standards. You will work across both structured data (BIM models from Autodesk Revit, 3D geometries, spatial relationships) and unstructured data (construction drawings, specifications, submittals) to develop production-grade ML solutions. This position offers a unique science breadth, spanning graph neural networks, natural language processing, reinforcement learning, and computer vision, with the opportunity to research and apply novel techniques against real-world engineering challenges at unprecedented scale. You will collaborate closely with architects, structural engineers, MEP engineers, construction managers, UX designers, product managers, and software development teams to translate research innovations into deployed systems that deliver measurable customer impact. The ideal candidate combines strong theoretical foundations in machine learning with practical experience applying ML to domain-specific problems. You understand that AECO professionals maintain exceptionally high trust bars and require AI systems that augment rather than replace professional judgment. You thrive on solving hard problems at the intersection of ML research and domain expertise. Key job responsibilities - Drive end-to-end GenAI projects with high complexity and ambiguity from conception to production - Build, adapt, evaluate, and deploy ML models for BIM and AECO applications, collaborating with software engineers for productionization - Research innovative machine learning approaches and identify new opportunities for GenAI applications in the building engineering and design domain - Design and implement model architectures, train and fine-tune foundation models using domain-specific datasets, and optimize performance through iterative experimentation - Process and prepare training data, including data governance, provenance tracking, quality checks, and creating reusable data pipelines - Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements for AECO applications - Represent the team in the broader science community, publishing findings, delivering tech talks, and staying at the forefront of GenAI and BIM research - Collaborate with a cross-functional team of ML scientists, building engineers, software engineers, UX designers, and product managers, and present results to senior leadership A day in the life About AIS AWS Infrastructure Services (AIS) owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. 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. This job opportunity is available both for the full time and part time employment.

We are working on improving shopping on Amazon using the conversational capabilities of large language models and through customer behavioral data to make them more personalized for each customer. We are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. In this role, you will be managing a team working on Large Language Model (LLM) and/or Vision-Language Model (VLM) post-training and alignment for new shopping experiences. You’ll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you’re fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. We are the Sponsored Products - Marketplace Intelligence (MI) team. We are looking for an Applied Scientist to help build production ML and bandit solutions to customize the search experience. We determine which ads to show in Amazon search, where to place them, how many ads to place, and to which customers. This helps shoppers discover new products while helping advertisers put their products in front of the right customers, aligning shoppers’, advertisers’, and Amazon’s interests. To do this, we apply a broad range of machine learning, causal inference, and optimization techniques to continuously explore, learn, and optimize the allocation and ranking of ads on the search page. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the SP experience in search by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. You will be on the Search Ad Ranking and Interleaving team org - specifically the team that focusses on whole page optimization. Our mission is to personalize and contextualize SP ad allocation on the entire search page. We do this by modeling shopper responses to the number, placement, and quality of ads. We are a data- and hypothesis-driven organization that uses online experimentation, simulation, causal modeling, and online feedback to place ads where they’re useful to shoppers and provide improved discoverability and sales for advertisers. This is a unique opportunity for someone who wants to have broad business impact, a direct impact on customers and the search experience, and get broad exposure to a wide range of scientific techniques (machine learning, bandit learning, optimization, LLMs). We are looking for an Applied Scientist to join Interleaving team in Marketplace Intelligence with a broad mandate to experiment and innovate to grow Sponsored Products. We’d like someone with practical experience with LLMs / GenAI for production to improve how we rank and allocate ads on the page today. If you thrive in a product-focussed and data-driven environment, then this role is for you. As a Applied Scientist on this team, you will help to identify unique opportunities to create customized and delightful shopping experience for our growing marketplaces worldwide. Your job will be to identify big opportunities for the team that can help to grow Sponsored Products business working with retail partner teams, product managers, software engineers and TPMs. You will have opportunity to design, run and analyze / experiments to improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills in an environment that thrives on creativity, experimentation, and product innovation. Key job responsibilities * Tackle and solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. * Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. * Develop efficient algorithms for multi-objective optimization and AI control methods to find operating points for the ad marketplace then evolve them * Be an expert at designing and implementing solutions that use a range of data science methodologies to automate data analysis or to solve complex business problems. * Perform hands-on analysis and modeling of enormous data sets to develop insights that improve shopper experience, without compromising Ad revenue in addition to designing metrics for complex systems. * Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. * Run A/B experiments, gather data, and perform statistical analysis.

The Amazon Q Developer Science team is looking for an Applied Scientist who is passionate about building services and tools for developers that leverage artificial intelligence (AI) agents and machine learning (ML). You will be part of a team building AI-based services for Amazon Q Developer with the focus on redefining the way developer work. The team works in close collaboration with other AWS AI services such as AWS Bedrock, the AWS IDE Toolkit, and Amazon Sagemaker. If you are excited about working in cloud computing and building new AWS services, then we'd love to talk to you. Key job responsibilities As a senior Applied Scientist, you are recognized for your expertise, advise team members on a range of machine learning topics, and work closely with software engineers to drive the delivery of end-to-end modeling solutions. Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined. The problems that you take on require scientific breakthroughs. You take a long-term view of the business objectives, product roadmaps, technologies, and how they should evolve. You drive mindful discussions with customers, engineers, and scientist peers. You bring perspective and provide context for current technology choices, and make recommendations on the right modeling and component design approach to achieve the desired customer experience and business outcome. 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. Utility Computing (UC) AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. 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 (gender diversity) conferences, inspire us to never stop embracing our uniqueness. 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. Mentorship and 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. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

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.

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.

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.

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.