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This position is for an 8–12 week full-time, on-site internship to be conducted in Summer 2027 (June–September 2027). *Minimum 12 weeks internship is required for all international hires (who require JP visa support). *Target Candidates: Students graduating in 2029 Do you want to see your research directly impact how millions of customers discover, browse, and purchase products on Amazon — across Japan and the globe? Amazon's Japan Store Tech team owns the science and technology behind cross-border shopping — product discovery, search relevance, personalization, and content experiences spanning dozens of marketplaces. We tackle problems at massive scale: multi-language signals, multi-marketplace data, and region-specific customer behaviors, all served at low latency to millions of daily shoppers. We're looking for current Bachelor or Master students with a passion for applied science and machine learning to join us as an Applied Scientist Intern in Summer 2027 to shape the future of customer experiences at scale. For this position, our Japan Store Tech team is looking for students with a specialization in one or more of the following research areas: machine learning, deep learning, natural language processing (NLP), information retrieval, recommender systems, computer vision, large language models (LLMs), generative AI, causal inference, experimentation and A/B testing, optimization, and more! As an Applied Scientist Intern, you'll develop novel models and algorithms, design and run experiments on live traffic, and own meaningful science contributions end-to-end. You'll also leverage and contribute to GenAI/LLM systems that power both customer-facing experiences and internal development tools. If you want to kickstart your science career at global scale — solving real customer problems alongside talented scientists and engineers in a collaborative, international environment — this is the place to start. Application & Assessment Deadline: Mid October, 2026 Selection Communication Begins: End October, 2026 Offer Extensions: December 2026 – March 2027 Key job responsibilities - Collaborate and communicate effectively with experienced cross-disciplinary Amazonians to design, develop, and deploy innovative machine learning models and scientific solutions that delight our customers, while participating in technical discussions to drive solutions forward. - Develop and implement scalable machine learning models and algorithms to improve product discovery, search relevance, personalization, or other customer-facing experiences. - Design and conduct experiments (offline and online) to validate hypotheses and measure the impact of proposed solutions. - Analyze large-scale datasets to identify patterns, generate insights, and inform model design decisions. - Leverage and contribute to the development of GenAI and LLM-powered tools to enhance customer experiences and development productivity while staying current with emerging technologies. - Write clean, maintainable, production-quality code following best practices. - Communicate research findings effectively through documentation, presentations, and technical papers. - Work in an agile environment and collaborate closely with software engineers to bring science solutions from prototype to production. A day in the life As an intern, you will be matched to a manager and a mentor and will have the opportunity to influence the evolution of Amazon's science and technology and lead critical projects early in your career. In addition to working on an impactful project, you will have the opportunity to engage with Amazonians for both personal and professional development, expand your network, and participate in activities with other interns throughout your internship. No matter the location of your internship, we give you the tools to own your project and learn in a real-world setting. Amazon internships are full-time positions, and interns should expect to work in office, Monday–Friday, up to 40 hours per week typically between 9am–6pm. Specific team norms around working hours will be communicated by your manager. Interns should not have other employment during the Amazon work-day.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. We are looking for an Applied Scientist to push the envelope of AI content generation. As a scientist at Prime Video, you will contribute directly to productions using innovative tools in computer vision, deep learning, and generative AI to transform entertainment experiences. The ideal candidate has deep knowledge in one of: graphics, deep learning, generative AI and/or reinforcement learning and experience applying them real-world problems. You understand tradeoffs between business needs and model complexity, and you take calculated risks in developing rapid prototypes and iterative model improvements. You are excited to learn from and alongside seasoned scientists, engineers, and business leaders. You are an excellent communicator and effectively translate technical findings into production systems and business action (and customer delight). Key job responsibilities • Build generative AI models that create production-ready content, including movie content, localized assets, and visual marketing materials used across Prime Video's global platform. • Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity. • Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models. • Run experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative machine learning approaches. • Share knowledge and research outcomes via internal and external conferences and journal publications A day in the life In this role, you will invent science and systems for content localization, generation, including graphics and machine learning-based modeling systems. You will work with a team of scientists and product managers to design customer-facing products, and you will work with technology teams to productize and maintain the associated solutions.

The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide fee strategy, quantify its impact, and ensure fees are accurately computed and explained for billions of transactions between Amazon selling partners and customers. We help build the foundations for growing selling partner businesses, bringing the best selection and prices to Amazon customers, and helping Amazon leaders make and implement high impact decisions that optimally balance profitability and growth. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact. As an data scientist on our team, this role will focus on the application of data analysis, econometrics, machine learning, and artificial intelligence to measure and predict Amazon's P&L, with emphasis on fee revenue. This blends the tools of data science, statistics, and ML/AI. Your work will shape not only how fees are decided, but how they are interpreted and planned. We are seeking scientists who are motivated by first principles, disciplined experimentation, and the technical challenge of deploying ideas at global scale. This is an opportunity to work on consequential problems where analytic rigor meets real-world complexity, and where your analysis, models, algorithms, and systems will directly influence the experience of millions of sellers. If you are driven to build elegant solutions to hard problems—and to see them operate in production at meaningful scale—we would welcome the opportunity to build with you. Key job responsibilities ** Translate ambiguous business challenges into well-defined scientific problems with measurable impact. ** Identify opportunities to improve fee revenue measurement, prediction, planning, structure, and level. ** Identify opportunities to improve measurement, and prediction of other items of the P&L, at appropriate levels of granularity. ** Design, develop, and deploy econometric or AI/ML models that improve our understanding of the relationship between fees and costs, or predict fee revenue, and other elements of the P&L. ** Partner closely with finance and fee strategy teams to formulate scientific questions, communicate results, and productionalize solutions. **Apply rigorous simulation methods to validate models and quantify business impact at scale. **Communicate scientific innovations and results clearly to cross-functional stakeholders and contribute to the broader internal and external scientific community through publications, talks, and technical artifacts. About the team Amazon’s third-party marketplace is a multibillion-dollar global service, connecting customers and sellers across through billions of transactions annually. The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide business fee strategy, ensure fees are accurately computed for millions of products, and improve the seller experience with AI tools that support any fee related contact (understanding, audit, and dispute). We build the scientific foundation that empowers sellers to grow their businesses with clarity and confidence. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact.

The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide fee strategy, quantify its impact, and ensure fees are accurately computed and explained for billions of transactions between Amazon selling partners and customers. We help build the foundations for growing selling partner businesses, bringing the best selection and prices to Amazon customers, and helping Amazon leaders make and implement high impact decisions that optimally balance profitability and growth. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact. As an data scientist on our team, this role will focus on the application of data analysis, econometrics, machine learning, and artificial intelligence to measure and predict Amazon's P&L, with emphasis on fee revenue. This blends the tools of data science, statistics, and ML/AI. Your work will shape not only how fees are decided, but how they are interpreted and planned. We are seeking scientists who are motivated by first principles, disciplined experimentation, and the technical challenge of deploying ideas at global scale. This is an opportunity to work on consequential problems where analytic rigor meets real-world complexity, and where your analysis, models, algorithms, and systems will directly influence the experience of millions of sellers. If you are driven to build elegant solutions to hard problems—and to see them operate in production at meaningful scale—we would welcome the opportunity to build with you. Key job responsibilities ** Translate ambiguous business challenges into well-defined scientific problems with measurable impact. ** Identify opportunities to improve fee revenue measurement, prediction, planning, structure, and level. ** Identify opportunities to improve measurement, and prediction of other items of the P&L, at appropriate levels of granularity. ** Design, develop, and deploy econometric or AI/ML models that improve our understanding of the relationship between fees and costs, or predict fee revenue, and other elements of the P&L. ** Partner closely with finance and fee strategy teams to formulate scientific questions, communicate results, and productionalize solutions. **Apply rigorous simulation methods to validate models and quantify business impact at scale. **Communicate scientific innovations and results clearly to cross-functional stakeholders and contribute to the broader internal and external scientific community through publications, talks, and technical artifacts. About the team Amazon’s third-party marketplace is a multibillion-dollar global service, connecting customers and sellers across through billions of transactions annually. The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide business fee strategy, ensure fees are accurately computed for millions of products, and improve the seller experience with AI tools that support any fee related contact (understanding, audit, and dispute). We build the scientific foundation that empowers sellers to grow their businesses with clarity and confidence. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact.

Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced ML systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real-world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning team for India Consumer Businesses. Machine Learning, Big Data and related quantitative sciences have been strategic to Amazon from the early years. Amazon has been a pioneer in areas such as recommendation engines, ecommerce fraud detection and large-scale optimization of fulfillment center operations. As Amazon has rapidly grown and diversified, the opportunity for applying machine learning has exploded. We have a very broad collection of practical problems where machine learning systems can dramatically improve the customer experience, reduce cost, and drive speed and automation. These include product bundle recommendations for millions of products, safeguarding financial transactions across by building the risk models, improving catalog quality via extracting product attribute values from structured/unstructured data for millions of products, enhancing address quality by powering customer suggestions We are developing state-of-the-art machine learning solutions to accelerate the Amazon India growth story. Amazon India is an exciting place to be at for a machine learning practitioner. We have the eagerness of a fresh startup to absorb machine learning solutions, and the scale of a mature firm to help support their development at the same time. As part of the India Machine Learning team, you will get to work alongside brilliant minds motivated to solve real-world machine learning problems that make a difference to millions of our customers. We encourage thought leadership and blue ocean thinking in ML. Key job responsibilities Use machine learning and analytical techniques to create scalable solutions for business problems Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes Design, develop, evaluate and deploy, innovative and highly scalable ML models Work closely with software engineering teams to drive real-time model implementations Work closely with business partners to identify problems and propose machine learning solutions Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model maintenance Work proactively with engineering teams and product managers to evangelize new algorithms and drive the implementation of large-scale complex ML models in production Leading projects and mentoring other scientists, engineers in the use of ML techniques About the team International Machine Learning Team is responsible for building novel ML solutions that attack India first (and other Emerging Markets across MENA and LatAm) problems and impact the bottom-line and top-line of India business. Learn more about our team from https://www.amazon.science/working-at-amazon/how-rajeev-rastogis-machine-learning-team-in-india-develops-innovations-for-customers-worldwide

Amazon's Artificial General Intelligence (AGI) organization is seeking a talented Applied Scientist to help build the next generation of large language models and conversational AI experiences. In this role, you will design, develop, and evaluate state-of-the-art machine learning models that power products used by hundreds of millions of customers worldwide. You will work with large-scale datasets, modern deep learning frameworks, and world-class scientists and engineers to take ideas from research prototype to production. This is a hands-on scientist role that combines rigorous applied research with the engineering excellence required to ship reliable, responsible AI at Amazon scale. Key job responsibilities - Design, implement, and evaluate machine learning and deep learning models to solve challenging problems in natural language understanding, generation, and conversational AI. - Develop scalable training and evaluation pipelines for large language models using large, real-world datasets. - Collaborate with scientists, engineers, and product teams to translate research advances into production systems that improve customer experiences. - Conduct rigorous experimentation, analyze results, and iterate rapidly to improve model quality, efficiency, and safety. - Stay current with the latest research, contribute novel ideas, and help raise the scientific bar of the team. - Champion responsible AI practices, including evaluation for fairness, robustness, and safety. About the team The AGI organization at Amazon is dedicated to advancing the science and engineering of large language models and generative AI. Our teams build foundation models and the systems around them, spanning pretraining, post-training, evaluation, and responsible AI. We are a collaborative group of scientists and engineers who value scientific rigor, customer obsession, and rapid iteration, and we work on some of the most impactful AI problems at Amazon scale.

We are seeking an Applied Scientist, Seller Abuse Prevention, to lead the development and implementation of advanced AI solutions that will transform how we prevent bad actors from operating in our store and enable Selling Partners to start and grow their business without fear of disruption, so that customers and Selling Partners across the globe trust us and have confidence in the integrity of Amazon's store. This role will focus on building risk detection models leveraging state-of-the-art AI, including small language models, to detect and prevent abuse of Amazon's catalog worldwide. You will design, develop, and deploy scalable AI solutions to proactively detect and prevent marketplace abuse throughout the seller lifecycle. You will work with massive-scale, multi-modal datasets spanning behavioral patterns, transactional histories, and behavioral data to build detection systems that are ahead of evolving adversarial tactics. Key job responsibilities * Design and build predictive risk detection models using advanced AI techniques, including Natural Language Processing including LLMs and agents to proactively identify bad actors and prevent marketplace abuse at scale * Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels * Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience * Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas * Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness A day in the life Day-to-day you can expect to: - Explore datasets to understand predictors and patterns of abuse - Work with product, program, and engineering stakeholders to build solutions into production that will last - Identify new and emerging abuse vectors as abusers get more sophisticated - Use search, graph, computer vision, NLP, and anomaly detection methodologies to automatically detect abusive actions. About the team Seller Abuse Prevention detects abuse across 4 distinct spaces of abuse: catalog, review, financial risk, and discovery/competitor abuse. Seller Abuse Prevention is embedded in a team of scientists that tackle cross-spanning risk prevention problems. The team has expertise across graph networks, LLMs/agents, and fraud detection.

As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon's real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. We are looking for an Applied Scientist to join our team that is building revolutionary enterprise applications leveraging machine learning, generative AI, and agentic AI to help millions of companies worldwide manage their day-to-day supply chain operations. Our mission is to accelerate our customers' businesses through intuitive, differentiated technology solutions that solve enduring supply chain challenges. We blend strategic vision with curiosity and Amazon's real-world operational experience to build opinionated, turnkey solutions that make the 'buy versus build' decision a no-brainer for our customers. As an Applied Scientist, you will design and develop state-of-the-art machine learning models and algorithms that power intelligent supply chain applications at global scale. You will work at the intersection of research and real-world product impact—translating scientific breakthroughs into production systems that serve millions of customers. We operate like a startup within AWS, offering you the opportunity to tackle unprecedented challenges while working with the latest technologies in deep learning, large language models, and optimization. If you are passionate about pushing the boundaries of applied science, thrive in ambiguous problem spaces, and want to shape the future of supply chain intelligence while having the backing of AWS's extensive resources, we want to hear from you. Key job responsibilities • Design, develop, and deploy machine learning models for demand forecasting, inventory optimization, anomaly detection, and supply chain decision-making, with guidance from senior scientists on novel problem spaces. • Contribute to GenAI and Agentic AI solutions that automate supply chain workflows; implement model components, pipelines, and evaluation harnesses. • Formulate well-scoped business problems as ML problems; define data requirements, model architectures, evaluation metrics, and experimentation plans. • Drive applied science projects end-to-end — from prototyping through offline evaluation, A/B testing, and production deployment — with increasing autonomy. • Publish research in top-tier conferences (NeurIPS, ICML, KDD, AAAI) and contribute to patent filings. • Raise the technical bar through code reviews, design feedback, and knowledge-sharing sessions. • Collaborate with engineering, product, and business stakeholders to translate scientific capabilities into customer-facing features; communicate results to technical and non-technical audiences. • Stay current with state-of-the-art AI/ML research; identify and propose opportunities to apply emerging techniques to existing problems. • Follow and contribute to best practices for experimentation, model validation, and responsible AI development. About the team The AWS Applied AI Solutions team builds enterprise applications that leverage Amazon's operational expertise to solve real-world supply chain challenges for millions of companies. We operate like a startup within AWS—moving fast, shipping iteratively using state-of-the-art AI technologies. We invest in your growth through mentorship from senior scientists, conference publication support, and internal science reading groups. Amazon values diverse experiences—even if you don't meet all preferred qualifications, we encourage you to apply. If your career hasn't followed a traditional path, don't let that stop you. ABOUT AWS: 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. 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. 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 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. 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 and AmazeCon conferences, inspire us to never stop embracing our uniqueness. 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.

Application deadline: Applications will be accepted on an ongoing basis Applied Scientists in AWS Automated Reasoning develop and apply bleeding-edge formal methods, automated reasoning techniques, and neurosymbolic approaches to ensure the security, reliability, and correctness of Amazon/AWS services and customer applications. Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality. At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Application areas span cloud infrastructure verification, cryptographic assurance, AI safety, drone safety, and formal guarantees for generative AI systems. Our methods range from interactive theorem proving and constraint solving to neuro-inspired proof search This is a unique opportunity to get in early on a fast-growing segment of the business and help shape the technology, product, and business. You will have a chance to utilize your deep technical expertise within a fast-moving environment and make a large business and customer impact. Key job responsibilities • Design and implement algorithms and formal methods for automated reasoning, including constraint solving, model checking, static analysis, theorem proving, and program synthesis to verify the correctness, security, and reliability of computing systems. • Solve large or significantly complex problems that require deep knowledge and scientific innovation in your domain; own strategic problem solving and take the lead on design, implementation, and delivery of solutions with long-term quantifiable impact. • Develop new decision procedures, heuristics, and search strategies that improve the scalability and accuracy of verification tools; build and deploy production-grade automated reasoning systems at Amazon scale. • Explore and apply generative AI and machine learning techniques to enhance automated reasoning capabilities, including learning-based heuristics for search and optimization, neural approaches to symbolic reasoning, and methods for verifying the correctness of AI-generated code. • Develop automated reasoning techniques for generative AI and agentic coding systems, including methods for ensuring the safety and alignment of autonomous software agents and applying formal guarantees to large language model outputs. • Conduct original research snd publish findings in peer-reviewed venues. • Work with customer teams to understand the nature of their software and the properties they need to establish; identify tools and methods capable of addressing verification needs, including novel analysis capabilities. • Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing deep knowledge and experience; collaborate with partner teams to translate verification capabilities into production systems. • Mentor scientists and engineers on formal methods, neurosymbolic techniques, and best practices for building reliable automated reasoning systems; assist in career development of others.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Prime Video Personalization and Discovery organization operates at a global scale and is a critical part of Prime Video’s flywheel. We are responsible for matching customers with the right content at the right time, at all touch points throughout customers' content discovery journey: from home page, title page, and search page, to showing recommendations at the end of an episode or a movie. We drive both immediate and long-term business objectives while delighting our customers. Key job responsibilities As a Sr. Manager, Applied Science, in the Prime Video Personalization and Discovery organization, you will be responsible for optimizing the complete customer experience, across the touch points throughout customers’ discovery journey. This includes building AI and optimization solutions, working with product, engineering teams to deliver the optimal balance of customer delight and business outcomes. About the team Prime Video Personalization and Discovery (PVPD) is dedicated to creating a highly personalized content discovery experience that not only delights our customers but also drives both short-and long-term business goals. Our scope includes personalized recommendations, search, marketing, and the advanced machine learning technology and infrastructure that underpins these experiences. Our mission is to automate and enhance customer engagement through personalization, using ML and Generative AI.