Careers

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. We are looking for a Senior Research Scientist to join our growing Sustainability team to drive the science behind value chain decarbonization. This role will establish Amazon's scientific methodologies for sector- and cross-sectoral decarbonization mechanisms, and establish benchmarks for automated validation and risk assessment. As a Senior Research Scientist, you will be responsible for independently leading assessments of environmental issues across the full spectrum of Amazon businesses and evaluating sustainability impacts across the value chain. You will independently develop quality frameworks and methodologies that enable Amazon to scale procurement of high-quality environmental interventions while maintaining scientific rigor and environmental integrity. Key job responsibilities - Develop quality assessment frameworks for complex environmental interventions, baseline-setting approaches, and measurement methodologies - Build quantitative benchmark and statistical models that enable scalable evaluation across heterogeneous data sources - Create attribution methodologies for supply chain interventions across Amazon's diverse footprint - Develop social and environmental safeguard criteria that integrate community impact assessments - Collaborate with cross-functional teams including procurement, sustainability operations, and business units to translate scientific methodologies into operational requirements - Work under the direction of senior business leaders while acting as lead Subject Matter Expert for value chain decarbonization science, including designing and leading research, data collection, modeling, documentation, interpretation, and validation About the team Diverse Experiences: World Wide Sustainability 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. Inclusive Team Culture: 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 (inclusive diversity) 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.

We are building the next generation of personalized shopping experiences at Amazon through deep understanding of hundreds of millions of customers, billions of products and intricate context. We set out to create a personal shopping partner that is knowledgeable, understands your preferences, and helps you find the right solution for your needs. Key job responsibilities As an Applied Scientist on the team, you will help lead science innovation across multiple teams. You will solve a breadth of problems, ranging from developing state of the art thinking-LLM-based techniques to reason about customers and products; creating semantic representations of products, customers, and context; developing cross-attentive LLM rankers ; training multi-objective ranking and optimization systems; deploying large scale real-time AI systems. You will build tech used by teams across Amazon, while also having a direct connection to hundreds of millions of customers using Amazon to shop. You will improves lives of customers through improvements on offline benchmarks as well as online experiments. About the team We build new experiences powered by Generative models and our new LLM-based AI stack, that provides personalized and multi-modal Natural Language interface to Amazon's billions of products for both hundreds of millions of retail customers as well as internal teams building AI experiences such as Rufus, Alexa+, Interests AI, and multiple confidential initiatives. Come join us on this journey!

AWS Elastic Compute Cloud (EC2) Capacity Org is looking for an experienced applied optimization expert. This leader will join the Optimization Science Team to design, implement, and scale decision-making algorithms to manage EC2’s virtual and physical capacity systems. EC2 Capacity owns EC2’s top-level customer satisfaction metric capacity availability and the forecasting & decision-making systems which 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 various decision-making systems, which manage the trade-off between capex and capacity availability while matching demand and supply at different planning horizons. The stakeholders and partners include engineering and product management orgs within EC2 as well as the AWS Infrastructure Supply Chain (AIS) organization. We are seeking an expert with a strong background in mathematical optimization with excellent modeling skills, and expertise in the numerical solution of continuous and discrete problems using exact and and heuristic methods applied to very large-scale problems. Experience with decision-making under uncertainty; e.g., robust or stochastic optimization is an advantage. Candidates at the OR/ML interface, and particularly those who have experience applying ML / Gen AI methods to enhance and improve optimization algorithms or optimization-based decision-making systems, are encouraged to apply. The candidate will apply their knowledge to match the end-customer demand for virtual machines to physical resource supply at horizons ranging from five minutes to 13 years. The variety of problems requires principled mathematical decomposition and a good interface design between inputs and outputs at various horizons. Navigating the ambiguity of design choices across horizons is a critical component of the role. In a typical project, we analyze large volumes of data, and then develop a prescriptive optimization model with inputs from ML or statistical models and business users. Our solution approaches are validated through simulations and / or production A/B tests. Being successful requires having the scientific breadth to understand the interactions between different phases of a project from data analysis through to production, including resolving issues after rollout. As a Senior Applied Scientist on the EC2 Optimization Science team, you are critical to the speed and excellence of the end-to-end deliveries of production systems with optimization-based analytical engines. You will be hands-on with the mathematical modeling and implementation, and will also contribute to the design of the engineering system with the scalability, extensibility, maintainability, and correctness of the optimization engine in mind. You will review approaches by other scientists and engineers in terms of business relevance, technical validity, engineering / science interface, and computational performance. You will mentor and lead junior scientists by example. Communicating your results to guide the direction of the business and working with software development teams to implement your ideas in code is key to success. You will write technical, and less frequently, business documents that influence engineering investments and business direction. Collaborating with other scientists, software engineers, and product managers, you will develop creative, novel, and data-driven approaches to improve our existing cloud compute offerings and define new ones in a fast-paced and quickly changing environment, improving the experience of our customers and impacting the bottom line of EC2. 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. 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 Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production Partner closely with other scientists to deliver large, multi-faceted technical projects Share and publish works with the broader scientific community through meetings and conferences Communicate clearly to both technical and non-technical audiences Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business 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 advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.

AWS is one of Amazon’s largest and fastest growing businesses, serving millions of customers in more than 190 countries. We use cloud computing to reshape the way global enterprises use information technology. We are looking for entrepreneurial, analytical, creative, flexible leaders to help us redefine the information technology industry. If you want to join a fast-paced, innovative team that is making history, this is the place for you. AWS Central Economics & Science (ACES) drives best practices for objectively applying economics and science in decision making across AWS. The team collaborates with AWS science and business teams to identify, frame, and analyze complex and ambiguous problems of the highest priority. Through data-driven insights and modeling, ACES supports strategic decision-making across the AWS global organization, including sales operations and business performance optimization. The ACES Sales Channels team is hiring an Applied Scientist (Senior or below) to advance our mission of providing rigorous, causal-inference-driven recommendations for AWS sales optimization. This role will focus on building ML systems with a causal modeling foundation, designing seller incentive mechanisms, and developing intervention strategies across the entire sales motion. Key job responsibilities • Causal ML System Development: Build and deploy machine learning models that emphasize causal inference, ensuring recommendations are grounded in valid interventions • Incentive Design: Define and model incentives that drive desirable behaviors across AWS sales channels, partner programs, and reseller ecosystems • Stakeholder Collaboration: Work with business stakeholders to understand requirements, validate approaches, and ensure practical applicability of scientific solutions • Scientific Rigor: Promote findings at internal conferences and contribute to the team's reputation for methodological excellence A day in the life The ACES Sales Channels team works on understanding and optimizing AWS's sales channels, both direct (generalist and specialist sellers) and indirect (partners and Marketplace). Our work falls into three core areas: developing rigorous causal measurement and modeling frameworks using cutting-edge economics and statistical methods; designing programs and incentives to improve customer and business outcomes; and building ML-based recommendation systems for sellers, partners, and other AWS stakeholders. 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.

Alexa Connections is looking for a passionate, talented, and inventive Applied Scientist to help build industry-leading technology with Large Language Models (LLMs) and multimodal systems, requiring strong deep learning and generative models knowledge. You will contribute to developing novel solutions and deliver high-quality results that impact Connections products and services. Key job responsibilities As an Applied Scientist with the Alexa Connections team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of digital assistant technology. You will leverage Amazon's heterogeneous data sources, unique and diverse international customer nuances and large-scale computing resources to accelerate advances in text, voice, and vision domains in a multimodal setup. The ideal candidate possesses a solid understanding of machine learning, natural language understanding, modern LLM architectures, LLM evaluation & tooling, and a passion for pushing boundaries in this vast and quickly evolving field. They thrive in fast-paced environments to tackle complex challenges, excel at swiftly delivering impactful solutions while iterating based on user feedback, and collaborate effectively with cross-functional teams. A day in the life * Analyze, understand, and model customer behavior and the customer experience based on large-scale data. * Build novel online & offline evaluation metrics and methodologies for multimodal personal digital assistants. * Fine-tune/post-train LLMs using techniques like SFT, DPO, RLHF, and RLAIF. * Set up experimentation frameworks for agile model analysis and A/B testing. * Collaborate with partner teams on LLM evaluation frameworks and post-training methodologies. * Contribute to end-to-end delivery of solutions from research to production, including reusable science components. * Communicate solutions clearly to partners and stakeholders. * Contribute to the scientific community through publications and community engagement.

Amazon has co-founded and signed The Climate Pledge, a commitment to reach net zero carbon by 2040. As a team, we leverage GenAI, sensors, smart home devices, cloud services, material science, and Alexa to build products that have a meaningful impact for customers and the climate. In alignment with this bold corporate goal, the Amazon Devices & Services organization is looking for a passionate, talented, and inventive Senior Applied Scientist to help build revolutionary products with potential for major societal impact. Great candidates for this position will have expertise in the areas of agentic AI applications, deep learning, time series analysis, LLMs, and multimodal systems. This includes experience designing autonomous AI agents that can reason, plan, and execute multi-step tasks, building tool-augmented LLM systems with access to external APIs and data sources, implementing multi-agent orchestration, and developing RAG architectures that combine LLMs with domain-specific knowledge bases. You will strive for simplicity and creativity, demonstrating high judgment backed by statistical proof. Key job responsibilities As a Senior Applied Scientist on the Energy Science team, you'll design and deploy agentic AI systems that autonomously analyze data, plan solutions, and execute recommendations. You'll build multi-agent architectures where specialized AI agents coordinate to solve complex optimization problems, and develop tool-augmented LLM applications that integrate with external data sources and APIs to deliver context-aware insights. Your work involves creating multimodal AI systems that synthesize diverse data streams, while implementing RAG pipelines that ground large language models in domain-specific knowledge bases. You'll apply advanced machine learning and deep learning techniques to time series analysis, forecasting, and pattern recognition. Beyond technical innovation, you'll drive end-to-end product development from research through production deployment, collaborating with cross-functional teams to translate AI capabilities into customer experiences. You'll establish rigorous experimentation frameworks to validate model performance and measure business impact, building AI-driven products with potential for major societal impact.

At Amazon, our SCOT Labs team owns and operates the experimentation platform that powers randomized controlled trials (RCTs) across Supply Chain Optimization Technologies (SCOT). We are the scientific gatekeepers for policy updates that govern how Amazon buys, stores, and moves billions of units of inventory worldwide. This is not traditional A/B testing: we are building the infrastructure and methodology to causally evaluate complex and interconnected supply chain interventions. Our platform runs experiments that span millions of products and hundreds of fulfillment nodes simultaneously, measuring the real-world impact of policy changes on inventory health, customer experience, and operational cost. We are also advancing the science of causal inference in supply chain settings by developing novel approaches to treatment effect estimation, interference modeling, and emulation techniques that allow us to assess policy impact faster and more accurately than ever before. The experiments you design and the methods you build here will directly determine which policies ship to production. These decisions influence hundreds of millions of dollars in weekly inventory investments, labor allocation for tens of thousands of associates, and Amazon's overall supply chain efficiency. Beyond operational impact, this team pushes the frontier of causal experimentation methodology and contributes to the broader scientific community with publications at top venues. If you are a scientist who wants to shape how one of the world's largest supply chains makes decisions — solving causal inference challenges in real-world settings no academic lab or startup can replicate — this is the team for you. Key job responsibilities - Partner with customer teams to design rigorous large-scale experiments (such as randomized controlled trials and quasi-experiments) to evaluate policy updates and model improvements across millions of products, hundreds of fulfillment nodes, and diverse business contexts - Lead the end-to-end experimentation lifecycle, from hypothesis formulation through analysis and stakeholder alignment, to inform production rollout decisions - Advance causal inference methodology for supply chain settings, including treatment effect estimation, interference modeling, and emulation techniques that accelerate policy evaluation - Build and maintain production-grade experimentation infrastructure and analytical tools using Python, SQL, Scala, and related technologies - Perform large-scale exploratory data analysis to uncover patterns, identify opportunities, and inform experimental design and policy development - Develop and scale supply chain emulation systems that model inventory dynamics end to end, enabling rapid offline evaluation of policy changes across millions of products without the cost and latency of live experiments - Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels - Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues A day in the life You might start the morning reviewing results from a randomized controlled trial running across millions of products, digging into causal estimates and designing the next iteration. Later, you could be designing an experiment with a partner team where interference is unavoidable: treated and control units share fulfillment networks and inventory pools, and you need a credible strategy despite the spillover effects. You'll build supply chain emulation systems that replicate inventory dynamics end to end, write code in Python, Scala, and SQL at a scale most scientists never encounter, and collaborate with scientists, engineers, and business teams across SCOT. Your research has a real chance of being published at top venues. The work is hard, the problems are unsolved, and the impact is immediate. If you want to do research that ships, this is where you do it. About the team The Forecasting and Labs Science team sits at the heart of Amazon's supply chain, building the science that determines what products are available, when, and at what cost for hundreds of millions of customers around the world. Our mission spans two deeply connected frontiers: pushing the boundaries of large-scale time series forecasting through foundation models that generalize across an enormous and diverse catalog of products, and building the experimentation and causal inference methodology that rigorously evaluates whether supply chain policy changes should ship to production. We are a team of scientists who care deeply about both research rigor and real-world outcomes. We don't just publish: we ship. And we don't just ship: we measure, iterate, and raise the bar. On the forecasting side, we build foundation models at a scale unmatched in industry, running experiments across millions of products and exploring novel data generation techniques that open new frontiers in model generalization. On the experimentation side, we design and run randomized controlled trials across hundreds of fulfillment nodes, advance causal inference in settings where interference is unavoidable, and build supply chain emulation systems that can evaluate policy changes in hours rather than months. Our work spans the full lifecycle: from foundational research and large-scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.

Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the next level. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. As a Research Scientist, you will work with a unique and gifted team developing exciting products for consumers and collaborate with cross-functional teams. Our team rewards intellectual curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the intersection of both academic and applied research in this product area, you have the opportunity to work together with some of the most talented scientists, engineers, and product managers. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Our team highly values work-life balance, mentorship and career growth. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We care about your career growth and strive to assign projects and offer training that will challenge you to become your best.