Careers

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining advertising through generative AI technologies, changing how customers discover products and engage with brands across Amazon.com and beyond. We combine human creativity with artificial intelligence to improve every stage of the advertising lifecycle, from ad creation and optimization to performance analysis and customer insights. We are dedicated to developing responsible AI technologies that balance advertiser needs and improve the shopping experience. In this role, you will develop rigorous methods to understand how traffic sources, customer journeys, and onsite experiences influence purchasing and revenue. You will use experimentation, causal inference, statistical analysis, and machine learning to distinguish correlation from true business impact and identify the drivers of performance changes. You will work with product, engineering, and business partners to translate complex findings into clear recommendations, design measurable interventions, and build tools that support faster diagnosis and better investment decisions. If you are energized by solving complex challenges at the intersection of AI and measurement science, we would love to talk to you. Key job responsibilities - Develop methods to understand how traffic sources, customer journeys, and onsite experiences drive purchasing and revenue, using experimentation, causal inference, and machine learning to separate correlation from true business impact. - Drive or heavily influence the design of scientifically complex software solutions or systems, taking ownership of components and providing system-wide design guidance for both new and evolving systems. - Define a long-term science vision and roadmap, combining science leadership, technical depth, and business understanding. - Translate complex findings into clear recommendations for product, engineering, and business partners, and build tools that support faster diagnosis and better investment decisions. - Mentor junior scientists, raise the scientific bar across the team, and identify opportunities where generative AI can accelerate learning and efficiency. A day in the life You might start your morning reviewing anomaly detection outputs to understand a recent shift in traffic patterns, then move into a working session with engineers to refine how an attribution model is served in production. After lunch, you could lead a design review for a new quasi-experimental framework with product and finance partners. Later, you might pair with a junior scientist on their causal inference approach, helping them sharpen their methodology before presenting results to leadership. About the team The Sponsored Products and Brands team builds solutions that extend advertising campaigns beyond the Amazon store, reaching shoppers across third-party websites and apps where they search and shop. We pair large-scale, low-latency systems with advanced machine learning to deliver high-quality sponsored experiences to advertisers and shoppers alike. Within this team, the measurement program is a growing area of investment focused on understanding how shoppers arrive at Amazon and how that traffic translates into advertiser and business results. This is a high-visibility role where your work will directly inform investment decisions and product strategy. We are a distributed team of scientists, engineers, and product managers who partner closely with other cross-functional teams across the organization. If you want to shape how Amazon measures and improves the customer journey, this is the team to join.

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.

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset. The Team Join the next science revolution at AWS Life Sciences Applied AI Solutions where you'll work alongside world-class scientists to build AI that transforms how therapeutics are discovered, developed, and brought to patients. We're out to revolutionize how medicines are discovered, developed, and brought to patients powered by a new generation of AI. Our team tackles some of the hardest open problems at the intersection of frontier AI and life sciences. We apply biological foundation models large language models and agentic reasoning systems to life sciences problems then put them into the hands of pharma biotech and diagnostics customers as applications and managed services they can fine-tune tailor and deploy on their own data. The science challenges are deep: how do you design agentic systems that reason correctly over complex biological regulatory and clinical logic? How do you enable customers to tailor foundation models to their proprietary data and get better outputs with less effort? How do you adapt models to reason faithfully in high-stakes scientific and regulatory domains? Today we're focused on two areas. In drug design, our products (including Amazon Bio Discovery) accelerate discovery by giving bench scientists AI-guided protein engineering and antibody design capabilities. In clinical trials we're building AI that automates and optimizes regulatory and clinical development workflows. We combine frontier research with production-scale delivery to put breakthrough science into the hands of customers solving humanity's hardest problems. We value scientific rigor encourage publication and support conference participation. If you want to do research that ships this is the team. Key job responsibilities • Set the scientific vision and research agenda for LLM reasoning, agentic AI, and biological model customization across the portfolio • Innovate on LLM reasoning, planning, and agentic approaches for complex scientific and regulatory workflows • Develop model customization methods (fine-tuning, RLHF, retrieval augmentation, domain adaptation) that enable customers to train better models on their own data with less effort • Advance methods to adapt and extend biological foundation models for customer-specific therapeutic applications • Solve open research problems in faithful reasoning, multi-step planning, and tool use in high-stakes scientific domains • Partner with Life Sciences domain experts and customers to understand their hardest scientific challenges and translate those into tractable research problems • Publish at top-tier venues and build the team's external scientific reputation • Mentor applied scientists across the team while maintaining significant personal research contribution • Collaborate with product and engineering to ensure research translates into shipped products that serve customers at scale • Influence multi-year research roadmaps through deep scientific expertise and customer understanding A day in the life • Push a new reasoning approach into production that measurably improves outputs for a pharma customer's workflow • Design and run experiments to validate a novel fine-tuning method then ship it as a capability customers can use immediately • Unblock a delivery milestone by diagnosing why a model is failing on a new class of inputs and implementing a fix • Meet with a customer's scientific team to scope what the next model release needs to do for them • Review a teammate's experimental results sharpen the approach and help get it over the finish line • Publish results from shipped work at a top venue closing the loop between research and impact • Prototype a new idea that could become the next major capability in the product About the team 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. 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. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud. 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 and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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 on ML teams building large-scale forecasting and optimization systems that power Amazon’s global transportation network and directly impact customer experience and cost. As an Applied Scientist II, you will set scientific direction, mentor applied scientists, and partner with engineering and product leaders to deliver production-grade ML solutions at massive scale. Key job responsibilities 1. Lead and grow a high-performing team of Applied Scientists, providing technical guidance, mentorship, and career development. 2. Define and own the scientific vision and roadmap for ML solutions powering large-scale transportation planning and execution. 3. Guide model and system design across a range of techniques, including tree-based models, deep learning (LSTMs, transformers), LLMs, and reinforcement learning. 4. Ensure models are production-ready, scalable, and robust through close partnership with stakeholders. Partner with Product, Operations, and Engineering leaders to enable proactive decision-making and corrective actions. 5. Own end-to-end business metrics, directly influencing customer experience, cost optimization, and network reliability. 6. Help contribute to the broader ML community through publications, conference submissions, and internal knowledge sharing. A day in the life Your day includes reviewing model performance and business metrics, guiding technical design and experimentation, mentoring scientists, and driving roadmap execution. You’ll balance near-term delivery with long-term innovation while ensuring solutions are robust, interpretable, and scalable. Ultimately, your work helps improve delivery reliability, reduce costs, and enhance the customer experience at massive scale.

Are you passionate about solving complex problems and protecting one of the world’s largest cloud platforms? The AWS Payments and Fraud Prevention team is looking for an innovative Applied Scientist to help keep AWS a safe and trusted environment for millions of customers worldwide. In this role, you will design, build, and deploy machine learning models that detect, prevent, and mitigate fraudulent activity across the AWS ecosystem. You will work with massive, real-world datasets, develop new detection strategies, and apply advanced and practical technologies to tackle ever-evolving threats. You will also explore Generative AI (GenAI) techniques to uncover new fraud patterns and strengthen our fraud defenses. At AWS, we support hundreds of thousands of businesses, powering billions of transactions every day. Fraudsters are constantly innovating — and so are we. If you enjoy thinking like a fraudster, building resilient defenses, and making a real-world impact, we invite you to join us and help shape the future of secure cloud computing. Key job responsibilities * Design, build, and deploy machine learning models to detect, prevent, and mitigate fraudulent activities across the AWS platform. * Analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats. * Explore and apply GenAI techniques, including large language models (LLMs), synthetic data generation, and adversarial simulations to enhance fraud detection capabilities. * Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions. * Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics. * Continuously monitor model performance and improve robustness against adversarial behaviors and evolving fraud tactics. * Communicate findings and technical insights clearly and effectively to both technical and non-technical audiences. * Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization. About the team AWS Payments and Fraud Prevent is responsible for detecting & mitigating AWS account risks. You’ll be part of a team of Scientists, Analysts, and Technical & non-Technical Program Managers. The team’s goal is to identify and neutralize fraudsters from unauthorized access to legitimate AWS customers accounts. We have a formal mentor search application that lets you find a mentor that works best for you. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members.

Are you passionate about building machine learning systems that protect one the world's largest cloud platform? The AWS Payments & Fraud Prevention (P&FP) Science team is looking for a driven Applied Scientist to help safeguard AWS and its millions of customers from evolving fraud threats. In this role, you will design, build, and deploy end-to-end machine learning models that detect and prevent fraudulent activity. You will work with massive, real-world datasets across the AWS payments, signup and usage ecosystem, develop new detection strategies, and take models from concept to production. You will also apply Generative AI (GenAI) techniques to enhance fraud signal discovery and strengthen our detection capabilities. At AWS, we process billions of transactions every day for hundreds of thousands of businesses worldwide. Fraud patterns shift constantly and our defenses must stay ahead. If you enjoy owning problems end-to-end, shipping models that make real-time decisions at scale, and making a direct impact on customer trust and financial protection, we invite you to join us and help shape the future of fraud prevention at AWS. Key job responsibilities Design, build, and deploy end-to-end machine learning models and rules to detect, prevent, and mitigate fraudulent activities across the AWS payment and usage ecosystem. Source, extract, and analyze large-scale behavioral, transactional, and historical datasets to uncover fraud patterns and emerging threats. Apply hands-on expertise in statistical modeling, traditional machine learning, and analytics to identify and isolate issues across the fraud landscape. Explore and apply GenAI techniques, including large language models (LLMs) and synthetic data generation, to enhance fraud detection capabilities. Own the full model lifecycle — from data extraction and feature engineering through evaluation, productionalization, and deployment. Continuously monitor model and rule performance and improve robustness against adversarial behaviors and evolving fraud tactics. Experiment, prototype, and iterate on new detection strategies, algorithms, and evaluation metrics with a focus on rapid time-to-production. Collaborate closely with engineering, product, and operations teams to translate business needs into scalable technical solutions. Communicate findings and technical insights clearly and effectively to both technical and non-technical stakeholders at all levels. Contribute to the broader fraud prevention strategy, driving innovation and best practices across the organization. A day in the life You will have the opportunity to enhance our existing models and develop new ones that have a direct impact on the business from reducing financial losses to protecting customer accounts in real time. You will own your models end-to-end, from sourcing data and building features through evaluation and productionalization, and you will be expected to move quickly as fraud threats evolve. As part of this role, you will also support core fraud operations reviewing model outputs, tuning detection thresholds, and ensuring our mechanisms are performing as expected in production. This operational closeness to the data and to real fraud cases is what gives our scientists a unique edge: you will develop a deep, practical understanding of how fraud actually works, which directly sharpens the models and strategies you build. It is this combination of science and operational insight that makes our team's work so impactful. Your role will also allow you to leverage your customer-obsession skills by thoughtfully considering the user experience and ensuring it is not adversely affected by the mechanisms you design. You will explore new techniques, including Generative AI, to stay ahead of increasingly sophisticated adversaries. About the team Our team plays a crucial role in safeguarding secure and profitable business operations. Our mission is to position AWS as the most cost-effective and user-friendly cloud service provider by protecting legitimate customer experiences from the financial, operational, and reputational impacts of fraudulent activities. We develop services that prevent, detect, contain, and mitigate the actions of fraudulent and malicious users. As part of an analytics-driven team, you will leverage large-scale data to inform business decisions, respond to fraud, and automate decision-making at scale. Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. About 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 (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Mentorship & 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.

Are you a scientist interested in pushing the state of the art in machine learning and recommendation systems? Are you interested in working on novel ideas that can positively impact millions of customers? Do you wish you had access to large datasets and tremendous computational resources? Answer yes to any of these questions and you will be a great fit for our team at Amazon. As an Senior Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for Personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Key job responsibilities - Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to optimize the shopping experience, and run these models directly on the retail website. - Develop AI solutions for Recommendation systems using Deep learning, LLMs, Reinforcement Learning, distillation, and Optimization methods; - Work closely with engineers and product managers to design, implement and launch AI 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;

Are you excited to figure out not just what is happening but why — and to help build a delivery business that's still taking shape? We're looking for a Data Scientist who thrives on ambiguity and wants to own measurement, modeling, and experimentation across how customers experience Amazon's drone-delivery service. Your work will span the full data-science toolkit: designing and analyzing experiments (A/B tests), deep-diving customer-experience issues to find root causes, building propensity and behavioral models, forecasting demand, and applying causal methods to understand what actually drives our metrics. You'll work with large, evolving operational and customer datasets; partner closely with data engineers, scientists, and business stakeholders; and translate rigorous analysis into clear, decision-ready recommendations. Because the business is early and moving fast, you'll help define the right problems as much as solve them — with real room to explore new methods and shape how we measure and improve as we scale. If you're a curious, collaborative problem-solver who's energized by turning complex, ambiguous data into insight and clear direction, we'd love to hear from you. Key job responsibilities - Design and execute data science solutions using a range of methodologies—including machine learning, statistical modeling, and generative AI techniques—to address business problems where the approach is not immediately clear. - Acquire, transform, and validate large, evolving operational and customer datasets dive deep to investigate anomalies and data quality; and partner with data engineers to bring models and metrics into production. - Design, run, and analyze experiments (A/B and quasi-experimental studies) to measure impact, size opportunities, and guide product and operational decisions. - Deep-dive customer-experience issues and metric movements to identify root causes — the why behind the what, including how our metrics and their drivers relate and translate findings into clear, actionable recommendations. - Communicate complex analyses to technical and non-technical audiences, earn the trust of senior leaders, and influence roadmap and prioritization decisions with your recommendations. - Own your workstream end-to-end, from problem definition through delivery and ongoing measurement partnering across data engineering, product, and business teams as the business scales. A day in the life You might start your morning reviewing model performance metrics before joining a working session with engineers to refine a data pipeline. After lunch, you could be prototyping a new machine learning approach, running experiments, and comparing results against baseline models. Later, you might present preliminary findings to business partners, translating statistical outputs into plain-language recommendations. You will regularly participate in team discussions, scientific reviews, and mentoring conversations that keep you learning and growing.

We’re pioneering frontier science solutions to detect and treat product quality issues and enhance post-order experience in Amazon. Our goal is to enhance support for our diverse seller community and foster improved outcomes for both sellers and consumers for broader Amazon ecosystem. We are looking for passionate innovators who are excited about technology, driven by customer experience, and eager to make a lasting impact on the industry. In this role, you'll collaborate with top-tier scientists, engineers, and technical program managers (TPMs) to drive innovation in GenAI foundation models, adapt Large language model to our domain, develop efficient tabular foundation model, innovate on behavior foundation model. You will lead the effort to leverage Amazon's large-scale computing resources to accelerate advances in GenAI and frontier ML solutions. If you’re enthusiastic about joining a dynamic and motivated team, this is your chance to be part of an exciting journey. Apply now and help us shape the future of seller support at Amazon! Key job responsibilities 1. Develop domain-specific foundation models. 2. Apply the domain-specific foundation model to product risk detection, seller interactions capturing, future seller behaviors prediction, and seller responses simulation across varied conditions. 3. Work with business and engineers to develop and deploy the solutions.

Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as Sr Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As Sr Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit https://www.amazon.science for more information Key job responsibilities Provide scientific and technical leadership in the design and development of scalable LLM-based solutions that address complex, ambiguous language challenges across the International Seller Services domain — setting the technical direction and raising the science bar for the team. Partner strategically with cross-functional leaders — software engineers, data scientists, and product managers — to shape project vision, define success metrics, and drive the delivery of high-impact solutions from concept to production. Lead deep, rigorous data analysis to surface insights, uncover patterns, and translate them into actionable recommendations that measurably improve seller performance and customer experiences across global marketplaces. Drive innovation by researching, evaluating, and championing state-of-the-art NLP and LLM techniques, and by influencing the adoption of best practices that advance the accuracy, efficiency, and scalability of our language systems. Serve as a trusted technical advisor — communicating complex scientific concepts with clarity to both technical and executive stakeholders, and guiding decision-making on solution trade-offs and their broader business impact. Mentor and elevate fellow scientists and engineers, fostering a culture of scientific excellence, peer review, and continuous learning across the organization. A day in the life Set the scientific direction - architect and lead the development of scalable LLM and NLP solutions that tackle the most complex, ambiguous language challenges in seller acquisition, content generation, and catalog understanding Turn frontier research into production reality - champion state-of-the-art techniques, prototype ambitious ideas, and partner with engineers to ship science that operates reliably at massive scale Harness data at global scale - leverage some of the richest e-commerce datasets in the world to uncover deep insights and translate them into actionable strategy that measurably moves the business Influence beyond your team - partner with leaders across product, engineering, and science to shape roadmaps, define success metrics, and align solutions that generalize across Europe, Japan, China and beyond Solve problems that matter - frame ambiguous business challenges into well-scoped science problems that directly serve sellers and customers worldwide Raise the bar - mentor and elevate fellow scientists and engineers, lead design and peer reviews, and foster a culture of scientific rigor and continuous learning Be a trusted technical voice - communicate complex concepts with clarity to both technical teams and senior executives, guiding key decisions on trade-offs and long-term impact