Careers

Do you want a role with deep meaning and the ability to make a major impact? As part of Intelligent Talent Acquisition (ITA), you'll have the opportunity to reinvent the hiring process and deliver unprecedented scale, sophistication, and accuracy for Amazon Talent Acquisition operations. ITA is an industry-leading people science and technology organization made up of scientists, engineers, analysts, product professionals and more, all with the shared goal of connecting the right people to the right jobs in a way that is fair and precise. Last year we delivered over 6 million online candidate assessments, and helped Amazon deliver billions of packages around the world by making it possible to hire hundreds of thousands of workers in the right quantity, at the right location and at exactly the right time. You’ll work on state-of-the-art research, advanced software tools, new AI systems, and machine learning algorithms, leveraging Amazon's in-house tech stack to bring innovative solutions to life. Join ITA in using technologies to transform the hiring landscape and make a meaningful difference in people's lives. Together, we can solve the world's toughest hiring problems. Key job responsibilities Design and implement machine learning models that power recommendation systems for job seekers and recruiters, ensuring high performance, scalability, and reliability at global scale. Our ideal candidate has a strong scientific foundation and experience of statistical analysis and model building and has a passion for fairness and explainability in ML systems. Collaborate with engineers, scientists, and product managers to define requirements, create solutions, and deliver products that improve the hiring experience. Participate in the full software development lifecycle including scoping, design, coding, testing, documentation, deployment, and maintenance of recommendation systems and ML models. Solve complex ML problems using optimal data structures and algorithms, making thoughtful trade-offs between efficiency and maintainability. Stay current with scientific literature and develop novel approaches that address business challenges in talent acquisition. You will have the opportunity to provide feedback on scientific work across the organization helping the entire Intelligent Talent Acquisition organization improve. A day in the life You might spend the morning reviewing a colleague’s code for a new recommendation algorithm feature, then collaborate with product managers to refine requirements for an upcoming enhancement. After lunch, you’ll dive into model development, analyzing performance metrics from recent A/B tests and implementing improvements to the job-seeker recommendation pipeline. Throughout the day, you’ll participate in scientific discussions with peers across the organization, providing valuable feedback while continuing to refine your expertise.

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.

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.

The Foundational AI Assets (FAI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities - Design and implement novel machine learning and deep learning models to address scientifically complex problems, taking solutions from research through production deployment while maintaining high standards for code quality and system scalability. - Drive the scientific agenda for your team by identifying new opportunities, proposing research initiatives, and securing alignment with technical leadership on strategic priorities. - Evaluate and improve model performance using rigorous experimental methodologies, applying techniques such as feature engineering, hyperparameter optimization, and advanced neural network architectures (e.g., Transformers, CNNs) built with frameworks like PyTorch or TensorFlow. - Mentor and coach scientists and engineers across the organization, conduct thorough code and design reviews, and contribute to the professional growth of your peers while raising the technical bar for the team. - Collaborate cross-functionally with engineering, product, and science partners to translate ambiguous business problems into well-defined scientific approaches, balancing short-term customer needs with long-term technical vision. A day in the life You might start your morning reviewing experiment results and refining a model architecture before joining a design review where you provide guidance on a colleague's approach to a new problem. After lunch, you dive into writing production-quality code for a critical component of your system, then shift to drafting a scientific document that outlines your team's next research direction. Throughout, you engage with engineers and scientists across teams, sharing your expertise and learning from theirs. About the team The FAI team has the mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

The Foundational AI Assets (FAI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs). Key job responsibilities As an Applied Scientist with the FAI team, you will support the development of RL Gyms, assess their usefulness for the frontier model advancement and build techniques to advance the state of the art with LLMs. You will support the foundational model development in an applied research role. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The FAI team has the mission to push the envelope in GenAI with LLMs, in order to provide the best-possible experience for our customers.

Amazon Advertising drives billions of ad impressions and millions of clicks daily, powering discovery and sales for advertisers across Amazon's Retail and Marketplace businesses. The Ads Marketing Decision Science team sits at the intersection of data science and marketing strategy. We build intelligent, data-driven systems that analyze advertiser behavior at large scale to deliver the right guidance to the right advertiser at the right time. Our work spans behavioral modeling, content intelligence, automated decision systems, and GenAI applications, enabling personalized marketing experiences that help advertisers make smarter advertising decisions and grow their business on Amazon. We are looking for a Data Scientist who brings strong fundamentals in machine learning, causal inference, and statistical modeling to solve real advertiser problems. You will build predictive models, design experiments, develop segmentation frameworks, and leverage GenAI capabilities where applicable, taking solutions end-to-end from proof-of-concept to production at scale. You will partner closely with scientists, engineers, and product managers on a daily basis to prototype rapidly, ensure data integrity in production systems, and deliver measurable advertiser impact. If you are passionate about solving real-world problems with cutting-edge science, come join us as we innovate and make history. Key job responsibilities • Define and execute data science solutions end-to-end, from problem framing through production deployment. • Build machine learning models (classification, regression, clustering, ranking) for advertiser segmentation, propensity modeling, and recommendations. • Apply causal inference and experimentation methods (A/B testing, difference-in-differences, propensity score matching) to measure the impact of marketing interventions. • Analyze large-scale advertiser behavioral data to identify trends, surface growth opportunities, and support optimal decision making. • Collaborate with colleagues across science and engineering disciplines for fast turnaround proof-of-concept prototyping at scale. • Establish and drive data hygiene best practices to ensure coherence and integrity of data feeding into production ML/AI solutions. • Leverage GenAI and LLM capabilities to enhance science products where applicable A day in the life You will solve real-world problems by analyzing large volumes of advertiser data, building predictive models, designing experiments, and measuring business impact. You will prototype rapidly, validate ideas with data, and partner with engineers to productize and scale successful solutions. You will collaborate daily with scientists, engineers, and product managers across the advertising organization, working in a cross-functional, fast-paced environment where data drives decisions and helps advertisers grow. About the team We are a team of Applied Scientists, Research Scientists, Data Scientists, and Business Intelligence Engineers with deep expertise in ML, NLP, Gen-AI, RL, and causal inference, from a diverse range of backgrounds. We partner closely with strong engineers, product managers, and sales leaders who bring ads-industry depth and experience building scalable modeling and software solutions.