Careers

Elevate Your Economic Research at the Forefront of Global Retail Innovation We're seeking a brilliant economics researcher to join our dynamic team in Tokyo, where your analytical skills will drive transformative insights across Amazon's global retail ecosystem. As an intern, you'll collaborate with world-class economists, data scientists, and business leaders to solve complex challenges that shape the future of e-commerce. A day in the life Your day will be filled with intellectual exploration and impactful problem-solving. You'll dive deep into large-scale datasets, develop sophisticated econometric models, and translate complex economic research into actionable business strategies. Expect to engage in collaborative discussions, leverage modern analytical tools, and contribute to projects that have real-world implications for our global customers.

As part of the AWS 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. The Team Our research team is tackling fundamental challenges in visual reasoning that combine generative AI and agentic frameworks. We're investigating novel approaches to how AI systems understand spatial relationships, reason about object interactions, and maintain contextual awareness across time. Working at the intersection of computer vision and large language models, we're developing theoretical frameworks and practical techniques that advance the state-of-the-art in visual AI. As part of the AWS Applied AI Solutions organization, we’re advancing the frontier of visual reasoning and agentic AI technologies. Our vision is to develop sophisticated AI systems that can understand, interpret, and reason about visual information at human-like levels, enabling breakthrough applications across multiple industries. Key job responsibilities Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. As an Applied Scientist, you will help solve a variety of technical challenges and mentor other scientists. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved at scale before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people. A key focus of this role will be developing and implementing advanced visual reasoning systems that can understand complex spatial relationships and object interactions in real-time. You'll work on designing autonomous AI agents that can make intelligent decisions based on visual inputs, understand customer behavior patterns, and adapt to dynamic retail environments. This includes developing systems that can perform complex scene understanding, reason about object permanence, and predict customer intentions through visual cues. About the team AWS Solutions As part of the AWS 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. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

Work at the intersection of Generative AI, AI Agents, and large-scale ML, helping build Amazon's world-class advertising business. Key job responsibilities • Lead and contribute to end-to-end ML initiatives with high ambiguity, scale, and complexity from problem formulation through production deployment • Develop and optimize forecasting models by performing hands-on analysis of large-scale datasets to improve ad delivery prediction accuracy and operational efficiency • Build, experiment, and deploy machine learning models through rapid prototyping, rigorous experimentation, and close collaboration with software engineering teams for seamless productization. • Design and execute A/B experiments, collect performance data, and conduct statistical analysis to validate model impact • Establish scalable ML infrastructure including automated pipelines for data processing, model training, validation, and serving • Advance the state of the art by researching innovative machine learning techniques and applying them to forecasting challenges About the team You'll join a highly motivated, collaborative, and entrepreneurial team with a broad mandate to experiment, innovate, and break new ground. Here, your work will directly influence advertiser success and shape the future of programmatic advertising.

Do you want to join an innovative team of scientists who leverage machine learning and statistical techniques to revolutionize how businesses discover and purchase products on Amazon? Are you passionate about building intelligent systems that understand and predict complex B2B customer needs? The Amazon Business team is looking for exceptional Applied Science to help shape the future of B2B commerce. Amazon Business is one of Amazon's fastest-growing initiatives focused on serving business customers, from individual professionals to large institutions, with unique and complex purchasing needs. Our customers require sophisticated solutions that go beyond traditional B2C experiences, including bulk purchasing, approval workflows, and business-grade service support. The AB-MSET Applied Science team focuses on building intelligent systems for delivering personalized, contextual service experiences throughout the customer lifecycle. We apply advanced machine learning techniques to develop sophisticated intent detection models for business customer service needs, create intelligent matching algorithms for optimal service routing based on multiple variables including customer value, maturity, effort, and issue complexity, build predictive models to enable proactive service interventions, design recommendation systems for self-service solutions, and develop ML models for automated service resolution. As an Applied Scientist on the team, you will design and develop state-of-the-art ML models for service intent classification, routing optimization, and customer experience personalization. You will analyze large-scale business customer interaction data to identify patterns and opportunities for automation, create scalable solutions for complex B2B service scenarios using advanced ML techniques, and work closely with engineering teams to implement and deploy models in production. You will collaborate with business stakeholders to identify opportunities for ML applications, establish automated processes for model development, validation, and maintenance, lead research initiatives to advance the state-of-the-art in B2B service science, and mentor other scientists and engineers in applying ML techniques to business problems.

The Marketing Measurement & Performance Support (MAPS) organization is looking for a Science Manager, interested in leading a team of Economists, Data Scientists and Applied Scientists in designing a measurement system to solve one of the most challenging business problems in marketing measurement. This exceptional leader will develop solutions combining experimental evidence, observational models and decision frameworks to redefine brand marketing measurement. The MAPS organization’s mission is to be the most trusted source of measurement science solutions to drive marketing investment decisions across Amazon. The MAPS team provides incrementality, efficiency measurement services and decision support to marketing stakeholders across Amazon’s Stores suit of businesses. MAPS applies industry leading causal inference models and designs experiments to measure omni-channel effectiveness of marketing campaigns from these businesses worldwide. Our outputs shape Amazon product and marketing teams’ decisions and therefore how Amazon customers see, use, and value their experience with Amazon. As a Science Manager, you will lead a team of scientists to develop state-of-the-art models, while collaborating with other scientists, businesses, marketers, and software teams to solve key challenges facing the teams. Such challenges include measuring the incremental impact of multi-channel marketing portfolios, estimating the impact on long term inter-related customer actions, and scaling measurement solutions for WW marketplaces. Unlike many companies who buy existing off-the-shelf marketing measurement systems, we are responsible for studying, designing, and building systems to serve Amazon’s suite of businesses. Our team members have an opportunity to be on the forefront of marketing measurement thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, economists and software developers in the business. Key job responsibilities In this role, you will be a people manager and a technical leader in Econometric research with significant scope, impact, and high visibility. You will own developing the next generation of Causal Marketing-Mix-Media (MMM) models combining experimental evidence with observational econometric techniques. Your solution will deliver to business leaders accurate and actionable incrementality estimates and recommendations to optimize their marketing portfolio. As a successful Science Manager, you can navigate ambiguity, lead problem solving, guide development of new frameworks, and credibly interface between technical teams and business stakeholders. You are an innovator who can push the limits on what’s scientifically possible with a razor sharp focus on measurable business impact. You will coach and guide scientists in your team across different job families including Economists, Data Scientists and Applied Scientists to grow the team’s talent and scale the impact of your work.

Interested to build the next generation Financial systems that can handle billions of dollars in transactions? Interested to build highly scalable next generation systems that could utilize Amazon Cloud? Massive data volume + complex business rules in a highly distributed and service oriented architecture, a world class information collection and delivery challenge. Our challenge is to deliver the software systems which accurately capture, process, and report on the huge volume of financial transactions that are generated each day as millions of customers make purchases, as thousands of Vendors and Partners are paid, as inventory moves in and out of warehouses, as commissions are calculated, and as taxes are collected in hundreds of jurisdictions worldwide. Key job responsibilities • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. A day in the life • Understand the business and discover actionable insights from large volumes of data through application of machine learning, statistics or causal inference. • Analyse and extract relevant information from large amounts of Amazon’s historical transactions data to help automate and optimize key processes • Research, develop and implement novel machine learning and statistical approaches for anomaly, theft, fraud, abusive and wasteful transactions detection. • Use machine learning and analytical techniques to create scalable solutions for business problems. • Identify new areas where machine learning can be applied for solving business problems. • Partner with developers and business teams to put your models in production. • Mentor other scientists and engineers in the use of ML techniques. About the team The FinAuto TFAW(theft, fraud, abuse, waste) team is part of FGBS Org and focuses on building applications utilizing machine learning models to identify and prevent theft, fraud, abusive and wasteful(TFAW) financial transactions across Amazon. Our mission is to prevent every single TFAW transaction. As a Machine Learning Scientist in the team, you will be driving the TFAW Sciences roadmap, conduct research to develop state-of-the-art solutions through a combination of data mining, statistical and machine learning techniques, and coordinate with Engineering team to put these models into production. You will need to collaborate effectively with internal stakeholders, cross-functional teams to solve problems, create operational efficiencies, and deliver successfully against high organizational standards.

AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Research Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases

AI is the most transformational technology of our time, capable of tackling some of humanity’s most challenging problems. That is why Amazon is investing in generative AI (GenAI) and the responsible development and deployment of large language models (LLMs) across all of our businesses. Come build the future of human-technology interaction with us. We are looking for a Research Scientist with strong technical skills which includes coding and natural language processing experience in dataset construction, training and evaluating models, and automatic processing of large datasets. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will work closely with cross-functional teams, including product managers, language engineers, and other scientists. Key job responsibilities Specifically, the Research Scientist will: • Ensure quality of speech/language/other data throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc. • Clean, analyze and select speech/language/other data to achieve goals • Build and test models that elevate the customer experience • Collaborate with colleagues from science, engineering and business backgrounds • Present proposals and results in a clear manner backed by data and coupled with actionable conclusions • Work with engineers to develop efficient data querying infrastructure for both offline and online use cases

This is currently a 12 month temporary contract opportunity with the possibility to extend to 24 months based on business needs. 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.