Careers

Do you ever struggle to explain your work? How's this: "Do you shop at Amazon? Do you know that box that says 'Add to Cart' and shows a price? Our team owns the model which picks that offer and the customer experience around the display of the offer price and the elements surrounding the 'Add to Cart' button which inform a purchase decision. We pick and display offers several billion times a day across all surfaces (mobile app, mobile web, desktop, Alexa shopping) worldwide." The mission of the Amazon Buying Experience organization is to be the world’s first and most trusted choice for every customer on earth to discover and evaluate any product or service. Our team blends machine learning models to rank and select the best offer from the most trusted merchant for all products sold on Amazon along with a world-class front-end user experience for offer comparison to our global customers. We are responsible for the experiences and services that enable developers, including our own, to create tailored shopping experiences for every customer, product, business and marketplace offered by Amazon. We build scalable and extensible frameworks which allow for many teams at Amazon to innovate within the Offers Experience in a federated manner. If you are passionate about influencing and delivering the next-generation Amazon customer buying experience, we want to meet you. We are looking for an Economist to join one of the most impactful and visible teams in Amazon. In this role you will work with business stakeholders throughout Amazon and provide technical direction and business expertise to strategize and help launch new businesses and features for our customers. You will use data to justify customer friendly decisions and present customer and financial impact of the changes we make to our stakeholders. You will work closely with other economists, engineers, product managers, applied scientists, TPMs, managers, and senior leadership team to understand and drive business impact. Successful candidates will have experience working on multiple projects with different stakeholders, make data driven decisions, have strong communication skills to interact with executives, non-technical and technical individuals and have a high technical bar along with a passion for people and project management. This is an opportunity to work with a team that drives one of the most coveted real estate in the E-commerce, the Amazon ‘Buy Box’ on Amazon Product Detail Page, Amazon Search Page , multiple buying widgets etc. on the Amazon desktop, mobile and tablet environments. Key job responsibilities Today, millions of third party sellers offer products alongside Amazon Retail though the Amazon Marketplace and compete to be featured on the ‘Add to Cart’ button (aka the ‘Buy Box’) on Amazon’s most valuable retail real estate – the Product Detail Page referenced above. The Featured Merchant Algorithm (FMA) teams owns the Tier – I systems and Machine Learning algorithms that selects the offer to be featured on the Buy Box and on Amazon Search Pages. The team’s mission is to ensure that the featured offer provides the best possible customer value based on factors including price, availability, delivery options and customer service. The Amazon ‘Buy Box’ is arguably the most valued real estate in the E-commerce world and we are looking for strong leaders to continue innovating for this customer facing functionality. We are looking for an Economist to join one of the most impactful and visible teams in Amazon. In this role you will work with business stakeholders throughout Amazon and provide technical direction and business expertise to strategize and help launch new businesses and features for our customers. You will use data to justify customer friendly decisions and present customer and financial impact of the changes we make to our stakeholders. You will work closely with other economists, engineers, product managers, applied scientists, TPMs, managers, and senior leadership team to understand and drive business impact.

Want to help Amazon tell its customer-centric story around the world and work in a highly cross-functional environment with economists, lawyers, scientists, public policy, public relations, and business teams? If yes, keep reading! You'll join a team of economists, engineers, and lawyers to develop economic analysis and evidence supporting legal and regulatory matters across all our lines of business worldwide—including retail, marketplace services, AWS, consumer experience, shopping and search, and operations. In this role, you will have exposure to complex regulatory issues that are of high strategic importance to the company and will develop significant expertise on the economics of Amazon’s business operations and the industries in which it operates. If you're an economist with a passion for the current legal and policy debate, strong practical judgment and creative problem-solving skills, a love of communicating economic ideas to non-technical audiences, a knack for distilling data and economic models into key insights, and a track record of delivering results fast, we want to talk to you! Key job responsibilities • Provide data-driven guidance on high-stakes legal and regulatory questions facing Amazon worldwide • Collaborate with economists, scientists, engineers, and non-technical partners on high-impact projects with global scope • Partner with global public policy teams to apply economic analyses to current policy debates on competition, AI, and related issues • Engage with external stakeholders to drive deeper understanding of Amazon’s business model and the value it develops for the economy • Support requests for economic analyses and data in ongoing regulatory and litigation matters worldwide • Synthesize business facts and data into compelling economic narratives, translating complex findings into actionable insights • Advise stakeholders across Amazon on a broad spectrum of complex and often novel economic issues • Conduct, direct, and coordinate all phases of research projects—defining key questions, evaluating methodology, executing analysis, and communicating results

Amazon Economics is seeking Reduced Form Causal Analysis (RFCA) Economist Interns who are passionate about applying econometric methods to solve real-world business challenges. RFCA represents the largest group of economists at Amazon, and these core econometric methods are fundamental to economic analysis across the company. In this a full-time internship (40 hours per week, with hourly compensation). You'll work with large-scale datasets to analyze causal relationships and inform strategic business decisions, gaining hands-on experience that's directly applicable to dissertation writing and future career placement. By applying to this role, you are automatically being considered for all our available RFCA internships in 2027. Key job responsibilities As an RFCA Economist Intern, you'll specialize in econometric analysis to determine causal relationships in complex business environments. Your responsibilities include: - Analyze large-scale datasets using advanced econometric techniques to solve complex business challenges - Applying econometric techniques such as regression analysis, binary variable models, cross-section and panel data analysis, instrumental variables, and treatment effects estimation - Utilizing advanced methods including differences-in-differences, propensity score matching, synthetic controls, and experimental design - Building datasets and performing data analysis at scale - Collaborating with economists, scientists, and business leaders to develop data-driven insights and strategic recommendations - Tackling diverse challenges including program evaluation, elasticity estimation, customer behavior analysis, and predictive modeling that accounts for seasonality and time trends - Build and refine comprehensive datasets for in-depth economic analysis - Present complex analytical findings to business leaders and stakeholders

Love using AI to solve big, messy problems that actually move a business? Come lead the science team that figures out where every advertiser is on their growth journey, and what to do next to help them achieve their goals. Advertising is one of the fastest growing parts of our business, and science sits at the heart of how we help brands succeed. We're looking for an experienced science leader to build, grow, and guide a team of applied scientists working on one of our most exciting challenges: figuring out, for each brand, the smartest next step they can take to grow, and turning that into a clear, personalized recommendation the people who work with those brands can act on right away. If you want real ownership, a team to grow, and problems that matter to millions of businesses, we'd love to talk. Key job responsibilities You'll lead and grow a team of applied scientists and software engineers working on an AI-first mission to understand where every advertiser is in their growth journey and turning that into clear, personalized guidance the people who work with those brands can act on. A big part of our work is understanding where each advertiser is in their growth journey using that to decide the right level of support each one requires to meet their goals. Day to day that means: Set the long-term scientific vision for modeling each advertiser's growth journey, pinpointing their best next opportunities, and shaping the multi-year roadmap to get there Guide the science that pulls many signals, a brand's health, retail, and advertising performance, into one unified, personalized set of recommendations for teams to act on. Hire, coach, and develop scientists and engineers, and build a team known for a high scientific bar. Partner with product and engineering to take models from early research into production, and make sure the guidance genuinely helps brands grow. Set quality standards, catch at-risk work early, and make the hard trade-off calls. A day in the life No two days look the same. You might start by reviewing a new modeling approach with your scientists, then meet with product and engineering partners to agree on what to build next. You'll dig into results, ask hard questions about whether a model is really moving the business, and coach a manager through a tricky call. Your customers are the teams who work directly with brands, and the brands themselves, who lean on your team's recommendations to decide where to invest and how to grow. About the team Sales AI is a central science and engineering organization within Amazon Advertising Sales. Our mission is to power selling motions and account team workflows with state-of-the-art AI and ML services. We're investing in a range of sales intelligence models, including advertiser insights, recommendations, and generative AI-powered applications woven throughout account team workflows. We care about a high scientific bar, real business impact, and taking ideas all the way into production. Most of all, we like working with curious people who want to drive real, lasting impact for our customers.

We are seeking a Research Scientist II to join our Electric Propulsion Subsystem team. In this role, you will be a significant and autonomous contributor, solving difficult engineering problems related to battery management systems (BMS) and energy storage. You will design, develop, and validate BMS hardware and algorithms from concept through production, working on high-voltage lithium-ion battery packs that push the boundaries of performance, safety, and energy density. This position requires an engineer who demonstrates strong technical depth in battery systems and BMS design while also exhibiting the system-level thinking and influence characteristic of our most impactful contributors. Key job responsibilities Design, develop, and validate BMS architectures for high-voltage lithium-ion battery packs from concept through volume production, including cell monitoring and sensing strategy, communication topology, and isolation design Develop safety controls, fault detection and diagnostic mechanisms, cell balancing routines, and thermal protection boundaries, and lead test plans validating BMS designs against safety standards (e.g., UL 2271, IEC 62619, UN 38.3) for functional safety, thermal resilience, and abuse tolerance Develop software algorithms for State of Charge (SOC), State of Health (SOH), and State of Power (SOP) estimation using machine learning models Support Hardware-in-the-Loop (HIL) testing, bench verification, and real-world data analysis to validate BMS algorithms and correlate models with physical pack performance Design charging infrastructure interfaces, including charge profile management, communication protocols, and interoperability with external charging systems Apply system-level thinking to understand how the BMS integrates with and influences the broader product architecture, including power electronics, thermal management, and vehicle control systems, and maintain high-quality design documentation (schematics, algorithm specs, analysis reports, test results)

Description The AWS Neuron Science Core Algorithm team is looking for talented Applied Scientists to push the frontier of hardware-aware machine learning for Trainium and Inferentia, the AWS Machine Learning accelerators. In this rare role at the intersection of LLM modeling, large-scale training systems, and hardware/datatype co-design, you own model and algorithm decisions jointly with AWS custom silicon. You will own solutions end-to-end from research through production, publish at top venues, and work alongside distinguished engineers and scientists in a strategic growth area for AWS. We actively work on these areas: - Low-precision training and inference: MXFP8, MXFP4, and sub-4-bit training and inference recipes, stochastic rounding, and Trn4 datatype exploration. - Trn-friendly architectures: model architectures that exploit hardware strengths without sacrificing quality. - System-aware optimizers & efficient distributed systems: efficient optimizers and distributed systems that give the best accuracy, co-designed with the hardware. - Foundation-model pre-training accuracy: end-to-end validation across model scales, catching training divergence early, and equivalence-checking tooling. - GenAI for systems: RL post-training for NKI kernel generation, mitigating reward-hacking and accelerating under low precision on Trn. Key job responsibilities - Own scientific problems end-to-end - from research and experimentation through production impact - applying rigorous evaluation to complex, ill-defined problems at large scale. - Develop production-quality code in PyTorch or JAX and integrate scientific components into large-scale training and inference systems with operational excellence and efficient resource usage. - Partner with foundation-model, engineering, and hardware-architecture teams so your findings directly inform what gets built into Trainium and shipped in the product stack. - Mentor fellow scientists and interns, give constructive peer reviews, and help shape team goals, priorities, and the technical roadmap. - Author and publish research at top peer-reviewed venues (ICLR, NeurIPS, ICML, MLSys) and engage the broader scientific community.

The AWS Neuron Science Core Algorithm team is looking for talented Applied Scientists to push the frontier of hardware-aware machine learning for Trainium and Inferentia, the AWS Machine Learning accelerators. In this rare role at the intersection of LLM modeling, large-scale training systems, and hardware/datatype co-design, you own model and algorithm decisions jointly with AWS custom silicon. You will own solutions end-to-end from research through production, publish at top venues, and work alongside distinguished engineers and scientists in a strategic growth area for AWS. We actively work on these areas: - Low-precision training and inference: MXFP8, MXFP4, and sub-4-bit training and inference recipes, stochastic rounding, and Trn4 datatype exploration. - Trn-friendly architectures: model architectures that exploit hardware strengths without sacrificing quality. - System-aware optimizers & efficient distributed systems: efficient optimizers and distributed system that gives best accuracy, co-designed with the hardware. - Foundation-model pre-training accuracy: end-to-end validation across model scales, catching training divergence early, and equivalence-checking tooling. - GenAI for systems: RL post-training for NKI kernel generation, mitigating reward-hacking and accelerating under low precision on Trn. Key job responsibilities - Own scientific problems end-to-end - from research and experimentation through production impact - applying rigorous evaluation to complex, ill-defined problems at large scale. - Develop production-quality code in PyTorch or JAX and integrate scientific components into large-scale training and inference systems with operational excellence and efficient resource usage. - Partner with foundation-model, engineering, and hardware-architecture teams so your findings directly inform what gets built into Trainium and shipped in the product stack. - Mentor fellow scientists and interns, give constructive peer reviews, and help shape team goals, priorities, and the technical roadmap. - Author and publish research at top peer-reviewed venues (ICLR, NeurIPS, ICML, MLSys) and engage the broader scientific community. A day in the life You might start your morning reviewing large-scale training runs — checking accuracy at a new low-precision datatype or debugging a divergence before it costs a run — then join a design discussion with engineering partners on how to land your recipe in the production stack. After lunch you could be whiteboarding a Trn-friendly architecture variant or an RL post-training approach for kernel generation with a teammate, then writing code to prototype it on Trainium. You will regularly present findings to the team and to leadership, review peers' and interns' work, and stay connected with the academic community. About the team AWS Neuron is the software of Trainium and Inferentia, the AWS Machine Learning chips. Inferentia delivers best-in-class ML inference performance at the lowest cost in the cloud to our AWS customers. Trainium is designed to deliver the best-in-class ML training performance at the lowest training cost in the cloud, and it's all being enabled by AWS Neuron. Neuron is a software that includes an ML compiler and native integration into popular ML frameworks. Our products are being used at scale with external customers like Anthropic and Databricks as well as internal customers like Amazon FMR, Amazon AGI, Amazon Bedrock, Amazon Robotics, Amazon Ads, and many more.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you'll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You'll get to work on projects that are fast-paced, challenging, and varied. You'll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We'll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader, improving how customers search for and discover what to watch. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Prime Video customers worldwide. Key job responsibilities Develop machine learning solutions for Prime Video Search systems, including query understanding, retrieval, and ranking, using deep learning, reinforcement learning, and optimization methods; Work closely with engineers and product managers to design, implement and launch machine learning solutions end-to-end; Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; Effectively communicate technical and non-technical ideas with teammates and stakeholders; Stay up-to-date with advancements and the latest modeling techniques in the field; Publish your research findings in top conferences and journals.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! We are looking for a self-motivated, passionate and resourceful Applied Science Manager to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will lead a strong science team and work closely with other science and engineering leaders, product and business partners together to build the best personalized customer experience for Prime Video. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Lead to develop AI solutions for various Prime Video recommendation and personalization systems using Deep learning, GenAI, Reinforcement Learning, recommendation system and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Hire and grow a science team working in this exciting video personalization domain. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various devices. We work closely with the engineering teams to launch our solutions in production.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.