Careers

The Kindle team is seeking innovative Data Scientist for improving the reading experience. Our team is dedicated to enhancing the book reading experience using advancements in Science to improve the book reading experience for Kindle customers. Key job responsibilities - Inspect science initiatives across Amazon to identify how these can be applied and scaled to book reading experience. - Participate in team design, scoping and prioritization discussions. You must be able to map a business goal to a scientific problem, and map business metrics to technical metrics. - Spearhead the design and implementation of new features and algorithms based on thorough research and collaboration with cross-functional teams. - You have expertise in one of the science disciplines, such as machine learning, natural language processing, computer vision, Deep learning - You are able to use reasonable assumptions, data, and customer requirements to solve problems. - You initiate the design, development, execution, and implementation of smaller components with input and guidance from team members. - You work with SDEs to deliver solutions into production to benefit customers or an area of the business. - You assume responsibility for the code in your components. You write secure, stable, testable, maintainable code with minimal defects. - You understand basic data structures, algorithms, model evaluation techniques, performance, and optimality tradeoffs. - You follow engineering and scientific method best practices. You get your designs, models, and code reviewed. You test your code and models thoroughly - You participate in team design, scoping and prioritization discussions. You are able to map a business goal to a scientific problem and map business metrics to technical metrics. - You invent, refine and develop your solutions to ensure they are meeting customer needs and team goals. - You keep current with research trends in your area of expertise and scrutinize your results. A day in the life You will solve customer problems through innovative solutions that leverage the advancements in science. You will work with a group of talented scientists on researching algorithm and running experiments to test solutions to improve our experience. This will involve collaboration with partner teams including engineering, PMs, and other scientists to discuss data quality, model development and productionizing the same. You will mentor other scientists, review and guide their work, help develop roadmaps for the team.

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

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

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 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.

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's Price Perception and Evaluation team is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to build and scale an advanced self-learning scientific price estimation and product understanding system, regularly generating fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. The Applied Scientist will work closely with other research scientists, machine learning experts, and economists to design and run experiments, research new algorithms, and find new ways to improve Seller Pricing to optimize the Customer experience. The Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. Key job responsibilities - Research and use of statistical techniques to create scalable solutions for business problems. - Design, build, and deploy effective and innovative ML solutions to provide low prices and increased selection for customers using scientifically-based methods and decision making. - Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production. - Establish scalable, efficient, automated processes for large scale data analyses, model development, validation and implementation. - Publish and present your work at internal and external scientific venues.

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.