Careers

Interested in modeling and understanding customer behavior through machine learning, artificial intelligence, and data mining over TB scale data with huge business impact on millions of customers? Join our team of Scientists and Engineers developing models to predict customer behavior and optimize the customer experience with Amazon Prime. This includes identifying who our customers are, modeling customer behavior, and creating personalization systems to optimize the experience. As an ML expert, you will partner directly with product owners to intake, build, and directly apply your modeling solutions. There are numerous scientific and technical challenges you will get to tackle in this role, such as global scalability of models, combinatorial optimization, cold start problem, accelerated experimentation, short/long term goals modeling, GenAI based content creation, foundation modeling, and multi-step optimization leading to reinforcement learning of the customer journey. We employ techniques from GenAI, LLMs, deep learning, supervised learning, bandits, optimization, and RL. As the central science team within Prime, our expertise gets routinely called upon to weigh in on a variety of topics. We also emphasize the need and value of scientific research and have developed a strong publication and patent record (internally/externally) which you will be a part of. You will also utilize and be exposed to the latest in AI/ML technologies and infrastructure: AWS technologies (EMR/Spark, Redshift, Sagemaker, DynamoDB, S3, ...), various AI/ML algorithms and techniques (GenAI, LLMs, transformers, sequential models, Neural Networks, supervised/unsupervised/semi-supervised/reinforcement learning), and statistical modeling techniques. Major responsibilities - Build and develop AI and machine learning models and supporting infrastructure at TB scale, in coordination with software engineering teams. - Leverage GenAI, LLMs, deep learning, reinforcement learning for building production AI/ML systems - Develop backtesting/offline policy estimation tools and integrate with reporting systems. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes. - Work closely with the business to understand their problem space, identify the opportunities and formulate the problems. - Use AI, machine learning, data mining, statistical techniques and others to create actionable, meaningful, and scalable solutions for the business problems. - Design, develop and evaluate highly innovative models and statistical approaches to understand and predict customer behavior and to solve business problems.

AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Are you an experienced in sustainability science professional who is passionate about making a big impact? Are you interested in a high impact role at the world’s preeminent cloud computing company? The AWS Sustainability Science team is hiring a Sr. Sustainability Scientist. This role will support our sustainability goals by developing scalable methods and models to assess and improve the environmental impacts of AWS data centers and cloud computing services from manufacturing, transportation, use, and end-of-life. This work will drive a deeper understanding of AWS’s environmental impacts, and enable strategic long-term planning. The ideal candidate will have industry experience in driving Life Cycle Assessments (LCAs) of products and services, a data science foundation in forecasting methods, possess a strong understanding of the GHG Protocol and carbon accounting practices, and have a first principles knowledge of cloud infrastructure . The candidate should be comfortable working with imperfect data, identifying sources of uncertainty, and finding public data to fill the gaps where needed. The successful candidate must have strong analytical skills and the ability to apply systems thinking to complex, fast moving problems. The candidate should have familiarity with LCA methods and applications. The successful candidate will act as a subject matter expert, including designing research, gathering data, interpreting results, and developing science-driven narratives. The candidate will operate within a cross-functional project team in gathering and analyzing large amounts of data. Key job responsibilities In this role you will be responsible for: * Developing Life Cycle Assessment (LCA) methodologies to ensure AWS has an accurate and credible basis for sustainability efforts. * Developing internal LCA models and forecasting methods for AWS infrastructure and services * Conducting deep dives to identify, evaluate, and incubate emerging technologies that are informed by the insights from the models built. * Collaborating with an engineering team to develop dashboards and tools to support long-term planning. * Building and developing relationships with internal and external stakeholders to drive programs to completion A day in the life Each day you will interact with different teams responsible for all aspects of cloud infrastructure. Your work will span the life cycle of our operations and allow you to influence how we develop and implement sustainability practices and new technology. You will have the opportunity to work on projects locally and globally. About the team Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience? Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you. We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms. Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions

Amazon.com’s Buyer Risk Prevention (BRP) mission is to make Amazon the safest and most trusted place worldwide to transact online. Amazon runs one of the most dynamic e-commerce marketplaces in the world, with nearly 2 million sellers worldwide selling hundreds of millions of items in ten countries. BRP safeguards every financial transaction across all Amazon sites. As such, BRP designs and builds the software systems, risk models, and operational processes that minimize risk and maximize trust in Amazon.com. The BRP organization is looking for an Applied Scientist for its Payment Risk Mining ML team, whose mission is to combine advanced machine learning techniques to detect negative customer experiences, improve system effectiveness, and prevent bad debt across Amazon. As an Applied Scientist in Risk Mining, you will be responsible for modeling complex problems, discovering insights, and building risk algorithms that identify opportunities through state-of-the-art techniques including statistical models, deep learning, large language models (LLMs), and agentic AI systems. You will explore and implement state of the art approaches such as graph neural networks, anomaly detection, GenAI-powered investigation agents, and multi-agent systems to automate fraud detection and prevention at scale. You will build and deploy production-ready models and automated systems to improve operational efficiency and reduce bad debt. You will collaborate effectively with business and product leaders within BRP and cross-functional teams to build scalable solutions. The ideal candidate should be able to apply a breadth of tools, and advanced ML techniques, from traditional machine learning to emerging agentic frameworks, to take ideas from experimentation to production, driving tangible improvements in fraud detection and prevention. The candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high-impact role with goals that directly impact the bottom line of the business. 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

If you are excited about applying your science and engineering skills in business problems in the space of Credit management, B2B Financial Service, and Payments, we invite you to consider this Applied Scientist opportunity within Amazon B2B Payments and Lending (ABPL). ABPL is seeking a Senior Applied Scientist who combines their scientific and technical expertise with business intuition to build flexible, performant, and global solutions for complex financial and risk problems. You will develop and deploy production models to enhance our product features & processes that will delight our customers. Key job responsibilities As a Sr. Applied Scientist, you will design and build systems that support financial products. You will work closely with business partners, software and data engineers to build and deploy scalable solutions that deliver exceptional value for our customers. You will utilize intellectual and technical capabilities, problem solving and analytical skills, and excellent communication to deliver customer value. You will partner with product and operations management to launch new, or improve existing, financial products within Amazon. Responsibilities include: - Understand business and product strategies, goals and objectives. Make recommendations for new techniques/strategies including GenAI innovations, agentic AI frameworks, and foundation models, and to improve customer experience and business outcomes - Apply advanced data mining, machine learning, and Generative AI techniques to create AI/ML capabilities and support Credit and Fraud Management - Source, incorporate, and analyze alternative data to drive innovation, utilizing GenAI and foundation models - Own production models (real time and batch), conduct code review and model monitoring to insist high bar of operating excellence and ensure high performant models - Conduct research and educate business, product, marketing and product teams on the implementation of models and GenAI innovations, enabling strategic decision making through AI-powered insights and automation A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Amazon Seller Assistant is our flagship GenAI-first, multi-agent system that reimagines Seller experience. Our vision is to provide each seller with a proactive, autonomous, agentic assistant that understands their business and helps them navigate the complexities of selling by anticipating their needs, surfacing insights, resolving issues, taking actions on their behalf, and helping them grow. Amazon Seller Assistant helps millions of sellers on Amazon serve billions of customers worldwide. We are seeking a world-class Data Scientist to help define and build the next generation of Amazon Seller Assistant. You will partner with top-tier scientists and engineers to launch production-grade agentic capabilities at Amazon's scale — owning your problem space end-to-end, from a crisp customer insight to a shipped product that millions of sellers rely on. Key job responsibilities • Own the product vision, strategy, and roadmap for a key Seller Assistant capability area. • Define and ship agentic experiences — reasoning, planning, memory, context engineering — that solve hard seller problems at scale. • Partner with scientists and engineers to translate frontier AI research into production-grade features sellers trust and depend on. • Design rigorous evaluation frameworks — automated and human-in-the-loop — to measure agent quality, accuracy, and business impact. • Deep-dive into seller data, identify unmet needs, and write compelling PRFAQs that set the direction for your team. • Drive cross-functional alignment across science, engineering, UX, and business teams to deliver with speed and quality. About the team Amazon Seller Assistant team operates at the very frontier of agentic AI and agentic commerce — not as a research group, but as a team shipping production-grade, multi-agent systems used by millions of sellers worldwide. We move with the urgency of a startup and the resources of the world's most customer-obsessed company, he latest breakthroughs in science and engineering into capabilities that sellers rely on every day.

Amazon Japan Store Tech (JST) Science team serves as the core science division of JP Store Tech, with the vision to enable and accelerate the best-in-class CX through state-of-the-art machine learning technologies. This team owns the science vision definition, science roadmap planning, and science solution delivery in key business areas in Japan including Search, Customer Growth and Engagement, Personalization and Delivery. As an Applied Scientist, you will design, implement and deliver models on Amazon site, helping millions of customers every day to find quickly what they are looking for. You will propose innovation to build ML models trained on terabytes of product and traffic data, which are evaluated using both offline metrics as well as online metrics from A/B testing. You will then integrate these models into the production system that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times. Key job responsibilities * Invent or adapt new scientific approaches, models or algorithms inspired and driven by customers’ needs and benefits at the project level. * Analyze data and identify the gaps in existing solutions, and propose innovative science solutions. * Contribute to research papers that are published at peer-reviewed internal and/or external venues, and contribute to the wider scientific community. * Working with teams worldwide on global projects.

At Audible, we believe stories have the power to transform lives. It’s why we work with some of the world’s leading creators to produce and share audio storytelling with our millions of global listeners. We are dreamers and inventors who come from a wide range of backgrounds and experiences to empower and inspire each other. Imagine your future with us. ABOUT THIS ROLE As an Applied Scientist Intern at Audible, you will work alongside our science teams to solve problems spanning recommendation, content understanding, and AI-powered product experiences. You will solve large complex real-world problems at scale, draw inspiration from the latest science and technology to empower undefined/untapped business use cases, delve into customer requirements, collaborate with tech and product teams on design, and create production-ready models that span various domains, including Machine Learning (ML), Artificial Intelligence (AI), Natural Language Processing (NLP), Reinforcement Learning (RL), real-time and distributed systems. You'll apply ML/AI approaches to solve complex real-world problems while helping build the blueprint for how Audible works with AI. ABOUT YOU You are passionate about applying scientific approaches to real business challenges, with deep expertise in Machine Learning, Natural Language Processing, GenAI, and large language models. You thrive in collaborative environments where you can both build solutions and empower others to leverage AI effectively. You have a track record of developing production-ready models that balance scientific excellence with practical implementation. You're excited about not just building AI solutions, but also creating frameworks, evaluation methodologies, and knowledge management systems that elevate how entire organizations work with AI. As an Applied Scientist, you will... - Design and implement innovative AI solutions across our three pillars: driving internal productivity, building the blueprint for how Audible works with AI, and unlocking new value through ML & AI-powered product features - Develop machine learning models, frameworks, and evaluation methodologies that help teams streamline workflows, automate repetitive tasks, and leverage collective knowledge - Enable self-service workflow automation by developing tools that allow non-technical teams to implement their own solutions - Collaborate with product, design and engineering teams to rapidly prototype new product ideas that could unlock new audiences and revenue streams - Build evaluation frameworks to measure AI system quality, effectiveness, and business impact - Mentor and educate colleagues on AI best practices, helping raise the AI fluency across the organization ABOUT AUDIBLE Audible is the leading producer and provider of audio storytelling. We spark listeners’ imaginations, offering immersive, cinematic experiences full of inspiration and insight to enrich our customers daily lives. We are a global company with an entrepreneurial spirit. We are dreamers and inventors who are passionate about the positive impact Audible can make for our customers and our neighbors. This spirit courses throughout Audible, supporting a culture of creativity and inclusion built on our People Principles and our mission to build more equitable communities in the cities we call home.

Join us at the forefront of Amazon's sustainability initiatives to work on environmental and social advancements that support Amazon's long-term worldwide sustainability strategy. At Amazon, we're working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people who are passionate about making a meaningful impact on communities and the environment while helping shape the future of sustainable business practices. Sustainability Science and Innovation (SSI) is a multi-disciplinary team within WW Sustainability combining science, analytics, economics, statistics, machine learning, product development, and engineering expertise. We use data across the sustainability imperatives (carbon, water, waste, biodiversity, environmental risk and more) and these skills and capabilities to identify, develop, experiment, and scale the scientific solutions and innovations necessary for Amazon, customers and partners to help them solve their hardest unmet and evolving sustainability needs and goals. We are seeking an exceptional scientific leader to join Amazon's Sustainability Science and Innovation team as a Senior Researcher for Autonomous Materials Innovation. This role combines Physical AI with materials chemistry to accelerate the discovery and validation of sustainable materials through autonomous science systems. As a Senior Researcher in our Sustainability Materials Innovation Lab, you will lead the design and implementation of autonomous experimental research platforms that leverage data science and AI to accelerate materials innovation. You will establish scientific strategy and technical roadmaps for next-generation autonomous capabilities while leading research initiatives that tackle complex sustainability challenges in critical industrial sectors. This position requires driving breakthrough solutions in materials and energy sciences through strategic partnerships with universities, industry scientists, and government laboratories. You will mentor junior scientists and engineers while collaborating across Amazon's Innovation Lab Network to translate research into scalable solutions. Your leadership will be essential in developing early-stage, cost-effective technologies that address significant technical and economic challenges fundamental to Amazon's operations, requiring you to navigate complex trade-offs between immediate deliverables and long-term environmental impact. The ideal candidate demonstrates extensive experience in autonomous experimentation, materials or chemical sciences, and AI-driven research methodologies. You must possess proven ability to lead cross-functional teams, establish research priorities, and drive scientific innovation from concept to implementation. Deep technical expertise in laboratory automation combined with strategic vision for translating research into practical applications is essential. Your work will establish new paradigms in sustainable materials discovery at the intersection of Physical AI and materials chemistry, directly contributing to Amazon's sustainability goals while creating scalable solutions that extend beyond the company's immediate operations. Key job responsibilities - Develop scientific models that help solve complex and ambiguous sustainability problems, and extract strategic learnings from large datasets. - Work closely with applied scientists and software engineers to implement your scientific models. - Support early-stage strategic sustainability initiatives and effectively learn from, collaborate with, and influence stakeholders to scale-up high-value initiatives. - Support research and development of cross-cutting technologies for industrial decarbonization, including building the data foundation and analytics for new AI models. - Drive innovation in key focus areas including packaging materials, building materials, and alternative fuels. About the team Diverse Experiences: World Wide Sustainability (WWS) values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Inclusive Team Culture: It’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth: We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

As the Economist for Customer Promise, you will be responsible for leading the research, econometric modeling, and analysis to understand customer preferences that inform how the business operates. This entails developing analytic tools and economic models - including generative AI agents - that take into account inventory, fulfillment center capabilities, carrier capabilities, customer preferences, and economic impacts to determine customer promise. You will design, build, and rigorously validate GenAI agents against traditional econometric models and production experiments to assess their accuracy, interpretability, and operational impact. The models and agents you develop will drive changes in transportation and fulfillment networks. You will work with a diverse scientific team including computer scientists, machine learning engineers, and statisticians as well as other economists. You will build statistical models and AI-powered agents using world-class data systems to solve business problems in a fast-paced environment, continuously evaluating new methodologies against established econometric approaches to advance the state of the art in promise optimization. Key job responsibilities Conduct economic analysis and develop models that apply mathematical, econometric, and statistical techniques to business problems, including estimates, optimizations, and forecasts using established methodologies in your specialty area Partner with business stakeholders to understand their challenges and translate business questions into technical economic frameworks that deliver workable solutions Build and validate economic models by ensuring data quality, testing results using standard practices, and taking responsibility for correct implementation and the impact your analysis has on business decisions Communicate findings clearly through technical documents, reports, and presentations to leaders and colleagues, conveying your reasoning and limitations of your analysis with transparency Collaborate with other economists and technical teams to embed economic perspectives into projects and contribute to team goals and project-related metrics A day in the life In this role, you'll focus on building economic models and conducting data analysis that support critical business decisions. You might start your day by meeting with a business partner to understand a pricing question or operational challenge, then translate that into a technical framework. You'll spend time working with data analysis software like R, Matlab, or Stata—and potentially Python—to develop models, validate results, and ensure your analysis aligns with project goals. Throughout the day, you'll document your findings in clear technical reports, present your reasoning to colleagues, and collaborate with other economists to embed economic thinking into broader initiatives. You'll also stay current with developments in your economics specialty and participate in team discussions about methodology and best practices. About the team At Amazon, we are the most customer-centric company on earth. If you'd like to help us build the place to find and buy anything online, this is your chance to make history. To get there, we need exceptionally talented, bright, and driven people. We are looking for a dynamic, organized self-starter to join as an Economist for Customer Promise. The Customer Promise team seeks to identify the optimal delivery speed for whatever a customer wants. We believe that finding an optimal promise and living up to it consistently improves our customer experience because we increase customer's confidence and trust in Amazon as the one, best option to get what you want, when you want it.