Careers

Join our Amazon Private Brands Selection Guidance organization in building science and tech solutions at scale to delight our customers with products across our leading private brands such as Amazon Basics, Amazon Essentials, and by Amazon. The Selection Guidance team applies Generative AI, Machine Learning, Statistics, and Economics solutions to drive our private brands product assortment, strategic business decisions, and product inputs such as title, price, merchandising and ordering. We are an interdisciplinary team of Scientists, Economists, Engineers, and Product Managers incubating and building day one solutions using novel technology, to solve some of the toughest business problems at Amazon. As a Data Scientist you will investigate business problems using data, invent novel solutions and prototypes, and directly contribute to bringing your ideas to life through production implementation. Current research areas include named entity recognition, product substitutes, pricing optimization, agentic AI, and large language models. You will review and guide scientists across the team on their designs and implementations, and raise the team bar for science research and prototypes. This is a unique, high visibility opportunity for someone who wants to develop ambitious science solutions and have direct business and customer impact. Key job responsibilities Partner with business stakeholders to deeply understand APB business problems and frame ambiguous business problems as science problems and solutions. Perform data analysis and build data pipelines to drive business decisions. Invent novel science solutions, develop prototypes, and deploy production software to solve business problems. Review and guide science solutions across the team. Publish and socialize your and the team's research across Amazon and external avenues as appropriate Leverage industry best practices to establish repeatable applied science practices, principles & processes.

Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies

Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience? Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies

Amazon Braket is investing in fault-tolerant quantum computing capabilities. We are looking for an Applied Scientist to own resource estimation and workload benchmarking for fault-tolerant quantum workloads on AWS. You will answer the fundamental questions: how many physical qubits are needed, what gate depths are achievable, and what error budgets are realistic for a given algorithm on a given device. Your models will inform technical decisions, customer conversations, and our roadmap. This role requires more than resource estimation methodology alone. You need a broad foundation in quantum error correction research to reason about the full picture: how code choices affect resource requirements, how logical circuit structure impacts physical costs, and how benchmarking results feed back into the system. You will be part of a small team of scientists and engineers, and we expect you to codify your solutions in production-quality code and contribute directly to the codebase alongside your teammates. Key job responsibilities - Build and maintain FTQC resource estimation models that determine qubit counts, gate depths, and error budgets for target algorithms - Develop benchmarking frameworks that evaluate compilation quality against realistic hardware constraints - Produce resource estimates that inform technical decisions and feed into customer readiness work led by the Applications & Engagement team - Collaborate with the QEC compilation scientists on how resource estimates feed back into code selection and optimization - Connect benchmarking outputs to published materials in coordination with Braket's science and product teams - Stay current with the rapidly evolving QEC literature and incorporate new results into estimation models About the team Diverse Experiences AWS 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. 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 AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through novel 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 ecosystem. 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. Key job responsibilities * Hire and develop a team of high performing applied scientists that is responsible for developing AI solutions for Sponsored Brands advertiser and shopper experiences. * You should be technically solid, able to roll up your sleeves easily, and have strong curiosity and appetite to invent Gen AI solutions that solve customer problems. * Build recommendation systems that leverage generative models to develop and improve campaigns. * Technically lead and mentor the scientists on the team. * Research and apply the latest Gen AI and ML techniques and best practices. * Use analytical techniques to create scalable solutions for business problems. * Establish structures that enable your team to solve complex problems and deliver results. About the team We are on a mission to make Amazon the best in class destination for shoppers to discover, engage, and purchase relevant products, from brands that are relevant to them. In this role, you will design and implement Gen AI solutions that help millions of advertisers create more effective ad campaigns with intelligent recommendations, while improving the overall experience at Amazon's global scale. Our team invents, defines, and delivers advertising products that drive brand discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon Store businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, fast-paced, and collaborative team with an entrepreneurial spirit.

As a Data Scientist in Alexa Connections, you will lead the end-to-end development of machine learning and data science solutions that power intelligent communication experiences across channels such as calling, messaging and email. You will partner closely with product, engineering, and business leaders to translate ambiguous problems into scalable ML models, experimentation frameworks, and data-driven product decisions. In this role, you will design and deploy advanced ML and statistical models for capabilities such as prioritization, intent detection, and proactive action recommendations. You will analyze large-scale datasets and run rigorous experiments, including A/B testing and causal analysis, to measure impact and continuously improve customer engagement and product performance. Additionally, you will shape the applied science roadmap and collaborate with global cross-functional teams to deliver AI-driven solutions that scale to millions of Alexa customers. Key job responsibilities - Partner with product, engineering, operations, and security teams to translate complex business problems into scalable, production-ready data science and ML solutions. - Own the full lifecycle of model development, including exploratory analysis, model design, deployment, monitoring, and continuous improvement. - Define and track success metrics, conduct rigorous analyses, and provide insights that guide product launches, feature improvements, and data-driven decision-making. - Build data-driven business cases to prioritize science initiatives and demonstrate measurable impact of ML solutions. - Develop ML-powered systems supporting key business areas. - Lead research and analysis to understand customer interactions with Alexa, and enhance overall customer experience. - Contribute to the broader science community by mentoring analysts, improving data workflows and tooling, and publishing technical work in internal and external forums. A day in the life • Deep dive into our business metrics, analyze data, trends, and reviewing dashboards • Writing code: building packages in Python, writing SQL queries, deploying solutions for Connections experience teams to consume. • Leading or joining working sessions with Product Managers to refine problem statements new initiatives. • Exploring new features and model architectures, leveraging AWS services, documentation, and upskilling yourself to the latest technologies. • Leverage pre-trained LLMs to build applications that solve business problems for Connections experiences. • Meet with Sr. Engineers/Principal Engineers to align on solution designs. • Own or co-own MBR, WBR documents that are reviewed with Connections leadership team.

We are seeking a Human-Robot Interaction (HRI) Research Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun. In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans.

Are you excited about leading a team of scientists to solve complex, high-impact problems using machine learning and data-driven approaches? At Amazon Advertising Sales, we are looking for an experienced Applied Science Manager who can build, grow, and guide a team that delivers research and production-quality models at scale. In this role, you will own the strategic direction of your team's scientific roadmap, partner closely with business and technical leaders, and ensure your team's work directly shapes important decisions and customer-facing products. You will bridge the gap between science, technology, and business — translating ambiguous challenges into clear research agendas and actionable results. We are specifically looking to build AI agents that can operate accurately at large scale, helping transform the way account teams operate and optimize their end-to-end workflows. We are looking for a deeply scientific leader that brings their expertise in Natural Language Processing, Large Language Models, Reinforcement Learning, Deep learning and/or Recommender Systems to bring exciting new products come to life. If you are a thoughtful, collaborative leader who thrives on turning scientific innovation into measurable impact, we want to hear from you. Key job responsibilities - Lead and manage a team of applied scientists and analysts, setting the strategic direction and roadmap for scientific research that influences organizational goals and annual planning processes. - Partner with business and technical stakeholders to define vision, priorities, and success criteria — ensuring your team builds the right solutions at the right level of fidelity and that final outputs are ready to inform business decisions or move to production. - Evaluate and improve machine learning model accuracy and performance using rigorous experimentation, feature engineering, and hyperparameter optimization, while establishing a team culture focused on reproducibility and scientific rigor. - Hire, develop, and mentor scientists and technical contributors, providing growth opportunities and empowering team members to take ownership of key workstreams and deliver results independently. - Identify opportunities for new analysis, efficiency improvements, and generative AI integration, allocating resources effectively and proactively mitigating risks before they become roadblocks. A day in the life You start your morning reviewing experiment results with your scientists, asking probing questions about model assumptions and business relevance. Mid-morning, you join a cross-functional meeting with engineering and product partners to align on priorities for an upcoming launch. After lunch, you conduct a one-on-one with a team member, coaching them on a new research proposal. Later, you draft a narrative summarizing your team's quarterly progress and outline next steps for leadership review. Throughout, you balance hands-on technical guidance with strategic planning to keep your team delivering high-quality science. About the team Sales AI is focused on applying scientific research and machine learning to solve real problems that matter to Amazon and its advertising customers. We value intellectual curiosity, collaboration, and a commitment to building inclusive, high-performing teams. We believe that great science happens when people with different perspectives work together toward a shared mission. As we continue to grow, you will play a key role in shaping the team's direction, expanding our capabilities, and ensuring our work has lasting impact across the business.

Amazon's Stores Economics and Science (SEAS) team is looking for an Applied Scientist III to help us find global optima where others settle for local ones. You will work alongside scientists and engineers with backgrounds spanning machine learning, natural language processing, information retrieval, statistics, economics, causal inference, and optimization to lower cost-to-serve, optimize selection, and apply emerging machine learning techniques across a wide range of Stores problems — all at a scale that directly shapes how customers shop. This is a role for someone who does not default to the most popular model but instead thinks carefully about which approach will produce the most reliable and scalable result for each problem. You will lead science initiatives end-to-end, translating complex business problems into mathematical frameworks, building large-scale algorithms, and deploying production solutions in partnership with product teams. If you want to do cross-discipline applied science that moves real metrics, we would like to talk. Key job responsibilities - Lead large-scale science initiatives from research through production, translating supply chain and marketplace problems into mathematical frameworks and deploying algorithms that delight Amazon customers. - Design and implement machine learning and statistical models — choosing the right method for each problem rather than defaulting to a single paradigm — and take ownership of their performance in production. - Drive your team's scientific agenda by identifying new research opportunities, proposing initiatives, and aligning with technical leadership on priorities. - Collaborate with scientists, engineers, and product teams across Amazon's Stores organization to prototype, validate, and scale solutions that accelerate partner teams' progress. - Mentor and coach fellow scientists through code reviews, design reviews, and authoring of internal technical documents and external publications. A day in the life You might start your morning analyzing experiment results from a model you recently deployed, deciding whether the estimates are robust enough to move to an online test. By mid-morning you are whiteboarding a new formulation with a colleague from a partner team. After lunch you review a pull request, suggesting a simpler algorithmic approach that cuts latency. Later you carve out time to write code for a prototype that tests a new idea for reducing cost-to-serve. Your work regularly moves between hands-on modeling and the collaborative work of aligning your team around a shared scientific roadmap. About the team Stores Economics and Science (SEAS) is an interdisciplinary team within Amazon's Stores organization. Our mission is to apply science and engineering to move from local to global optima in methods, models, and software. We prove concepts at small scale first, then build solutions that work at Amazon scale — and we help partner teams across the company short-circuit months of research and development. Our team includes a high concentration of Amazon Scholars, and we actively collaborate with academia. We value intellectual curiosity, open collaboration, and thoughtful problem-solving. We encourage publishing, invest in mentorship, and support your growth as both a researcher and a builder.

We are looking for an Applied Science Manager III to lead a team of applied scientists building next-generation AI-powered seller risk detection systems. This role sits at the intersection of deep technical science, strategic planning, and cross-organizational partnership. GRIP Science serves a complex stakeholder landscape: enforcement teams, policy owners, marketplace operations, peer science orgs, and senior leadership, each with distinct timelines and priorities. The right leader for this role will navigate that landscape with clarity, balancing urgent operational needs against the long-term scientific investments required to stay ahead of adversarial actors at scale. You will independently own the vision, roadmap, and execution for a critical detection surface, defining what to build, what to defer, and how to measure success. You will set and maintain a high bar for scientific rigor while cultivating a team culture that rewards creative problem-solving and principled risk-taking. Key job responsibilities Strategy & Stakeholder Partnership • Define the scientific vision and multi-year roadmap for your team's detection domain, ensuring alignment across a broad set of stakeholders with competing priorities and timelines • Develop strong working relationships with enforcement, policy, operations, and engineering partners; serve as the trusted scientific voice in cross-functional discussions • Exercise judgment in sequencing work. Distinguish between high-urgency requests that warrant immediate response and those better addressed through systematic, durable solutions • Influence org-level planning (OP1/OP2) through compelling written narratives and data-driven proposals that articulate both near-term deliverables and long-term scientific bets • Proactively communicate trade-offs to leadership and stakeholders, building trust through transparency about what your team will and will not prioritize in a given cycle Science & Technical Leadership • Lead a team building state-of-the-art detection models that leverage modern AI techniques, including deep learning, foundation models, and generative AI, to identify novel abuse patterns at scale • Set and uphold a high standard for scientific methodology: model validation, experimentation rigor, reproducibility, and interpretability of results • Evaluate end-to-end scientific proposals for soundness, scalability, and production readiness; know when to push for deeper investigation and when to ship • Foster a culture of scientific curiosity and invention. Encourage the team to challenge assumptions, explore out of the box approaches, and invest in research that creates step-function improvements rather than incremental gains • Identify where emerging AI capabilities (e.g., large language models, multimodal reasoning, self-supervised learning) can be applied to unlock new detection signals or fundamentally reshape existing pipelines • Make informed trade-offs between model sophistication, delivery timelines, and operational sustainability Execution & Operational Excellence • Scope scientific projects, set priorities, and drive schedules; readjust decisively when incoming signals warrant a change in direction • Structure work to reduce exposure to classic failure modes: ambiguous requirements, untested assumptions, under-instrumented models • Establish mechanisms and metrics to continuously measure model effectiveness, enforcement precision, and downstream business outcomes • Drive production readiness: monitoring, alerting, on-call practices, and rapid response for AI/ML systems processing millions of daily decisions Team Building & Development • Build and lead a high-performing team of applied scientists; attract and develop talent that combines technical depth with ownership mindset • Invest in the growth of individual scientists through deliberate project assignment, regular coaching, and actionable feedback tied to career progression • Create an environment where scientists have the space to do deep work, the support to take calculated risks, and the recognition when they deliver • Manage performance across the full spectrum. Accelerate top performers toward promotion readiness while addressing gaps constructively and with clear expectations About the team GRIP Science (Global Risk Intelligence & Prevention) is the core detection engine within TSI Science. We build and operate state-of-the-art ML and AI models that identify and block bad sellers across Amazon's 24 global marketplaces. Our systems cover the full seller journey, from registration through every action on the marketplace, and score holistic risk across all active sellers. We push the frontier of adversarial detection by combining large-scale deep learning, graph-based reasoning, generative AI, and real-time inference to outpace bad actors who continuously evolve their tactics. This is one of the most technically demanding and high-impact applied science problem spaces at Amazon. Your models directly shape marketplace integrity for hundreds of millions of customers worldwide.