Careers

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through novel generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace ecosystem. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities As a Senior Applied Scientist on our team, you will * Develop AI solutions for Sponsored Brands advertiser and shopper experiences. Build recommendation systems that leverage generative models to develop and improve campaigns. * You invent and design new solutions for scientifically-complex problem areas and/or opportunities in new business initiatives. * You drive or heavily influence the design of scientifically-complex software solutions or systems, for which you personally write significant parts of the critical scientific novelty. You take ownership of these components, providing a system-wide view and design guidance. These systems or solutions can be brand new or evolve from existing ones. * Define a long-term science vision and roadmap for our Sponsored Brands advertising business, driven from our customers' needs, translating that direction into specific plans for applied scientists and engineering teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. * Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; * Design and conduct A/B experiments to evaluate proposed solutions based on in-depth data analyses; * Think big about the arc of development of Gen AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems * Effectively communicate technical and non-technical ideas with teammates and stakeholders; * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Stay up-to-date with advancements and the latest modeling techniques in the field About the team We are on a mission to make Amazon the best in class destination for shoppers to discover, engage, and purchase relevant products, from brands that are relevant to them. In this role, you will design and implement Gen AI solutions that help millions of advertisers create more effective ad campaigns with intelligent recommendations, while improving the overall experience at Amazon's global scale.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Amazon Ads Response Prediction team is your choice, if you want to join a highly motivated, collaborative, and fun-loving team with a strong entrepreneurial spirit and bias for action. We are seeking an experienced and motivated Machine Learning Applied Scientist who loves to innovate at the intersection of customer experience, deep learning, and high-scale machine-learning systems. Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. We are looking for a talented Machine Learning Applied Scientist for our Amazon Ads Response Prediction team to grow the business. We are providing advanced real-time machine learning services to connect shoppers with right ads on all platforms and surfaces worldwide. Through the deep understanding of both shoppers and products, we help shoppers discover new products they love, be the most efficient way for advertisers to meet their customers, and helps Amazon continuously innovate on behalf of all customers. Key job responsibilities As a Machine Learning Applied Scientist, you will: * Conduct deep data analysis to derive insights to the business, and identify gaps and new opportunities * Develop scalable and effective machine-learning models and optimization strategies to solve business problems * Run regular A/B experiments, gather data, and perform statistical analysis * Work closely with software engineers to deliver end-to-end solutions into production * Improve the scalability, efficiency and automation of large-scale data analytics, model training, deployment and serving * Conduct research on new machine-learning modeling to optimize all aspects of Sponsored Products and Brands business About the team We are pioneers in applying advanced machine learning and generative AI algorithms in Sponsored Products and Brands business. We empower every customer with a customized discovery experiences from back-end optimization (such as customized response prediction models) to front-end CX innovation (such as widgets), to help shoppers feel understood and shop efficiently on and off Amazon.

We are seeking a Robotics/AI Motor Control Scientist to develop cutting-edge machine learning algorithms for motor control systems in robots. In this role, you will focus on creating and optimizing intelligent motor control strategies to enable robots to perform complex, whole-body tasks. Your contributions will be essential in advancing robotics by enabling fluid, reliable, and safe interactions between robots and their environments. Key job responsibilities - Develop controllers that leverage reinforcement learning, imitation learning, or other advanced AI techniques to achieve natural, robust, and adaptive motor behaviors - Collaborate with multi-disciplinary teams to integrate motor control systems with robotic hardware, ensuring alignment with real-world constraints such as actuator dynamics and energy efficiency - Use simulation and real-world testing to refine and validate control algorithms - Stay updated on advancements in robotics, AI, and control systems to apply advanced techniques to robotic motion challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you. an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON DEVELOPMENT CENTER U.S., INC. Offered Position: Data Scientist II Job Location: New York, New York Job Number: AMZ9774210 Position Responsibilities: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. Position Requirements: Master’s degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and one year of experience in the job offered or a related occupation. Employer will accept a Bachelor’s degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and five years of progressive post-baccalaureate experience in the job offered or a related occupation as equivalent to the Master’s degree and one year of experience. Must have one year of experience in the following skill(s): (1) building statistical models and machine learning models using large datasets from multiple resources; (2) writing SQL scripts for analysis and data migration; and (3) applying specialized modelling software including R, Python, or MATLAB. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $169,541/year to $207,500/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000

The Selling Partner Communities (SPC) organization is dedicated to ensuring every Selling Partner (SP) achieves satisfaction with their Amazon selling experience. SPC serves as a vital bridge between sellers/vendors and Amazon, creating and managing platforms where they can connect, share knowledge, and receive help. The team's mission encompasses listening to SP feedback, advocating for experience improvements, and empowering SPs through effective, relevant, and timely communications that demonstrate Amazon's commitment to building lasting partnerships. We are seeking a Sr. Applied Scientist to join our Communities team. The successful candidate is a skilled scientist capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. Key job responsibilities The Senior Applied Scientist in the Selling Partner Communities (SPC) organization plays a crucial role in developing and implementing advanced AI and machine learning solutions to enhance the selling partner experience on Amazon. This role requires strong ownership of all technical aspects throughout the full project lifecycle, while partnering closely with the business to define requirements and exercise good judgment in balancing speed and complexity. As a senior AS on our team you will: - Handle challenging problems that directly impact millions of selling partners - Independently collect and analyze data - Design, develop and deliver scalable models, using any necessary programming, machine learning, and statistical analysis software - Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research - Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software - Participate in design and implementation across teams to contribute to initiatives and develop optimal solutions that benefit the SPC organization - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Selling Partner Communities (SPC) organization serves as a vital bridge between SPs and Amazon, creating and managing platforms where sellers can connect, share knowledge, and receive help. The team's mission encompasses listening to feedback, advocating for experience improvements, and empowering SPs through effective, relevant, and timely communications that demonstrate Amazon's commitment to building lasting partnerships. Within SPC, the Science team plays a critical role in leveraging artificial intelligence and machine learning technologies to enhance seller experiences and drive data-driven decisions. The team serves as technical advisors helping shape the organization's scientific vision and strategy, identifying and tackling intrinsically hard, previously unsolved problems that require novel scientific approaches. The team focuses on developing comprehensive frameworks for measuring and monitoring seller sentiment, improving content discovery and engagement, enhancing operational efficiency for Community Assistance Managers (CAMs), bringing clarity to complex challenges through scientific expertise, and creating AI-powered tools for content generation and validation.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON WEB SERVICES, INC. Offered Position: Research Scientist II Job Location: Seattle, Washington Job Number: AMZ9698004 Position Responsibilities: Perform and support the main psychometric aspects of exam development and operations, including but not limited to automated test assembly, item and test analyses, optimal item bank design, job task analysis, standard setting, quality assurance, and project planning. Conduct main aspects of psychometric analysis in operational work including performing item analysis using psychometric methods, building optimal test forms and pools via optimization techniques, analyzing and monitoring item bank health, setting pass standards via standard setting studies, and supporting Job Task Analysis (JTA) to define and refresh test blueprints. Conduct main aspects of psychometric analysis in developing and applying statistical and psychometric modeling to evaluate and ensure AWS certification exams’ validity, reliability, applicability, efficiency, and accuracy. Participate in research projects to improve existing operational processes and quality using advanced techniques such as Machine Learning (ML), statistical modeling, Natural Language Processing (NLP), Generative Artificial Intelligence (GenAI), etc. Develop automation code using R or Python for psychometric workflow pipeline and other tasks to improve operational efficiencies. Present, interpret, and communicate the results of analyses to stakeholders through written and oral reports. Follow the accreditation standards set by ISO/IEC:2012 17024 and the National Council for Certifying Agencies (NCCA) as they relate to valid psychometric practices. Engage with the professional community through conferences and publications. Position Requirements: PhD or foreign equivalent degree in Statistics, Psychometrics, Educational Measurement, Quantitative Psychology, Data Science, Industrial-Organizational (I/O) Psychology, or a related field and one year of research or work experience in the job offered, or as a Research Scientist, Research Assistant, Software Engineer, or a related occupation. Must have 1 year of experience in the following skill(s): 1. large-scale education, licensure, or certification assessment programs. 2. operational psychometric tasks on large-scale education, licensure, or certification assessment programs including item analysis, equating and scaling, item response theory, classical test theory, form and pool assembly, item bank health analysis, standard setting, and job task analysis. 3. at least one of the complex test designs such as linear-on-the-fly testing (LOFT), computerized adaptive testing (CAT). 4. at least one of the following areas including machine learning (ML) or natural language processing (NLP). 5. Programming skills in at least one script-based programming language (R, Python). Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $136,000/year to $184,000/ year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities We are looking for an Applied Science Manager to lead the Insights & Prompt Generation vertical within the Conversational Discovery Experiences (CAX) team in Sponsored Products and Brands (SPB). This team owns prompt generation, quality, personalization, and coverage for Sponsored Prompts, a new conversational ad format powered by large language models (LLMs) that helps shoppers discover products across Amazon.com. As an Applied Science Manager, you will lead a team of applied scientists and engineers to build and scale the prompt generation pipeline, develop new prompt themes and quality frameworks, and drive coverage expansion across all surfaces. You will own the science roadmap for prompt generation and personalization. You will define the metrics that measure prompt effectiveness and drive experimentation to improve CTR, helpfulness, and advertiser outcomes. This role requires strong technical depth in NLP, LLMs, and information retrieval, combined with the ability to manage and grow a science team, set research direction, and influence product strategy. You will work across organizational boundaries with engineering, product, and business teams to translate science investments into measurable business impact.

日本の大学で機械学習や関連領域の研究に従事している学生の皆様に向けたフェローシッププログラムのご案内です。Amazon JapanのRetail Scienceチームでは、何百万人もの顧客にインパクトを与える価値あるテクノロジーに繋がるような、新しいプロトタイプやコンセプトを開発するプロジェクトに従事していただく学生を募集しています。プログラムは1ヶ月から3ヶ月の短期間のプロジェクトになります。 プロジェクトの対象となるテーマには、自然言語処理、表現学習、レコメンデーションシステム、因果推論といった領域が含まれますが、これらに限定されるわけではありません。プロジェクトは、チームのシニアサイエンティスト1名または複数名のガイダンスのもとで定義、遂行され、プロジェクト中は他のサイエンティストもメンターとしてフォローします。 学生の皆様が新しいモデルを考案したり、新しいテクノロジーを活用し実験する時間を最大化できるようにすることが目標です。そのため、プロジェクトではエンジニアリングやスケーリングよりも、プロトタイピングを行い具体的に概念実証を行うことに集中します。 また、Amazonでは論文出版も推奨しています。従事した研究開発活動の成果物として出版される論文には著者として参加することになります。 フェローシッププログラムは目黒の東京オフィスで、他のチームと一緒に行われます。Amazonは、プログラム期間中に必要なIT機器（ラップトップなど）、給与と通勤費を支給します。 Are you a current PhD student enrolled in a Japanese university researching Statistics, Machine Learning, Economics, or a related discipline? The Japan Retail Science team is looking for Fellows for short term (1-3 months) projects to develop new prototypes and concepts that can then be translated into meaningful technologies impacting millions of customers. In this position, you will be assigned a project to carry out from areas including but not limited to natural language processing, representation learning, recommender systems, or causal inference. The project will be defined and carried out under the supervision of one or more of our senior scientists, and you will be assigned another scientist as a mentor to follow you during the project. Our goal is to maximize the time you spend on inventing new models and experimenting with new techniques, so the work will concentrate on prototyping and creating a tangible proof of concept, rather than engineering and scaling. Amazon encourages publications, and you will be included as an author of any published manuscript. The fellowship will be carried out from our Tokyo office in Meguro together with the rest of the team. Amazon will provide the necessary IT equipment (laptop, etc.) for the duration of the fellowship, a salary, and commuting expenses. Key job responsibilities このフェローシップは、次世代のAIデータエージェントを可能にするために、大規模なデータログから自動的にセマンティックレイヤーを構築することに関するものです。私たちは、ビジネスユーザーがクエリを実行するクラスターを維持しており、そのログに完全にアクセスできます。私たちは、これらのログをマイニングして自動的にセマンティックレイヤーを構築することを提案します。これは、テーブルに何が含まれているか、それらがどのように関連しているか、ビジネスがメトリクスをどのように定義しているかについての構造化された記述です。このレイヤーがなければ、AIエージェントは曖昧な列名、重複するテーブル定義、または異なるチームが異なる方法で計算するメトリクスを区別する方法がないため、正確なSQLを確実に書くことができません。このレイヤーを手動で構築することは高コストで不完全であり、すぐに陳腐化します。私たちのアプローチは手動のキュレーションを回避します。ユーザーが時間をかけて書いたすべての結合、集計、フィルターは、列が実際に何を意味するか、テーブルがどのように接続されるか、メトリクスがどのように計算されるかについての暗黙的な知識をエンコードしています。このクエリコーパスを解析し統計的に分析することで、曖昧性解消ルール、合意されたメトリクス定義、検証済みのテーブル関係を自動的に抽出できます。セマンティックレイヤーにより、データチームはデータのオンボーディングや各変数のルールと定義の指定に帯域幅の大部分を費やす必要がなくなり、エンドカスタマーが自律的にデータをクエリするまでの時間を短縮できます。 This fellowship is about automatically constructing a semantic layer from large data logs to enable the next generation of AI data agents. We maintain clusters on which our business users run their queries, and we have full access to its logs. We propose mining those logs to automatically construct a semantic layer: a structured description of what our tables contain, how they relate, and how the business defines its metrics. Without this layer, AI agents cannot reliably write correct SQL because they have no way to distinguish between ambiguous column names, overlapping table definitions, or metrics that different teams compute differently. Building this layer manually is expensive, incomplete, and goes stale immediately. Our approach avoids manual curation: every join, aggregation, and filter our users have written over time encodes implicit knowledge about what columns actually mean, how tables connect, and how metrics are calculated. By parsing and statistically analyzing this query corpus, we can automatically extract disambiguation rules, agreed-upon metric definitions, and validated table relationships. The semantic layer will enable data teams to avoid having to spend most of their bandwidth on onboarding data and specifying the rules and definition for each variable, shortening the time for our end customers to autonomously query data. A day in the life チームの多くのメンバーは、午前9時くらいから10時半くらいまでの間に仕事を始め、夕方6時から7時には仕事を終えています。出席が必要なミーティングに参加していれば、勤務時間は自由に決められます。 パートタイムを希望する場合、勤務時間数は採用担当者とともに決定します。フルタイムの場合、労働時間は通常の契約通り週40時間となります。 The majority of the team starts working between 9 and 10.30am until 18-19. You will have complete flexibility to determine your working hours as long as you are present for the meetings where your attendance is required. Number of working hours will be agreed together with the hiring manager in case you want to pursue the Fellowship part-time. In case of full-time, working hours will be 40/week as per a standard contract. About the team 私たちのチームは、日本および世界のすべてのAmazonのベンダー企業に提供されるソリューションを支える製品を発明し、開発しています。私たちは、プロダクトマネージャーやビジネス関係者と協力し、科学的なモデルを開発し、インパクトのあるアプリケーションに繋げることで、Amazonのベンダー企業がより速く成長し、顧客により良いサービスを提供できるようにします。 私たちは、科学者同士のコラボレーションが重要であり、孤立した状態で仕事をしても、幸せなチームにはならないと考えています。私たちは、科学者が専門性を高め、最先端の技術についていけるよう、社内の仕組みを通じて継続的に学ぶことに重きを置いています。私たちの目標は、世界中のAmazonのベンダーソリューションの主要なサイエンスチームとなることです。 Our team invents and develops products powering the solutions offered to all Amazon vendors, in Japan and worldwide. We interact with Product Managers and Business stakeholders to develop rigorous science models that are linked to impactful applications helping Amazon vendors grow faster and better serving their customers. We believe that collaboration between scientists is paramount, and working in isolation does not lead to a happy team. We place strong emphasis on continuous learning through internal mechanisms for our scientists to keep on growing their expertise and keep up with the state of the art. Our goal is to be primary science team for vendor solutions in Amazon, worldwide.

日本の大学で機械学習や関連領域の研究に従事している学生の皆様に向けたフェローシッププログラムのご案内です。Amazon JapanのRetail Scienceチームでは、何百万人もの顧客にインパクトを与える価値あるテクノロジーに繋がるような、新しいプロトタイプやコンセプトを開発するプロジェクトに従事していただく学生を募集しています。プログラムは1ヶ月から3ヶ月の短期間のプロジェクトになります。 プロジェクトの対象となるテーマには、自然言語処理、表現学習、レコメンデーションシステム、因果推論といった領域が含まれますが、これらに限定されるわけではありません。プロジェクトは、チームのシニアサイエンティスト1名または複数名のガイダンスのもとで定義、遂行され、プロジェクト中は他のサイエンティストもメンターとしてフォローします。 学生の皆様が新しいモデルを考案したり、新しいテクノロジーを活用し実験する時間を最大化できるようにすることが目標です。そのため、プロジェクトではエンジニアリングやスケーリングよりも、プロトタイピングを行い具体的に概念実証を行うことに集中します。 また、Amazonでは論文出版も推奨しています。従事した研究開発活動の成果物として出版される論文には著者として参加することになります。 フェローシッププログラムは目黒の東京オフィスで、他のチームと一緒に行われます。Amazonは、プログラム期間中に必要なIT機器（ラップトップなど）、給与と通勤費を支給します。 Are you a current PhD student enrolled in a Japanese university researching Statistics, Machine Learning, Economics, or a related discipline? The Japan Retail Science team is looking for Fellows for short term (1-3 months) projects to develop new prototypes and concepts that can then be translated into meaningful technologies impacting millions of customers. In this position, you will be assigned a project to carry out from areas including but not limited to natural language processing, representation learning, recommender systems, or causal inference. The project will be defined and carried out under the supervision of one or more of our senior scientists, and you will be assigned another scientist as a mentor to follow you during the project. Our goal is to maximize the time you spend on inventing new models and experimenting with new techniques, so the work will concentrate on prototyping and creating a tangible proof of concept, rather than engineering and scaling. Amazon encourages publications, and you will be included as an author of any published manuscript. The fellowship will be carried out from our Tokyo office in Meguro together with the rest of the team. Amazon will provide the necessary IT equipment (laptop, etc.) for the duration of the fellowship, a salary, and commuting expenses. Key job responsibilities このフェローシップは、エージェント型ショッピングの影響を研究することに関するものです。業界は、AIエージェントが消費者に代わって購買決定をますます仲介する近未来に収束しつつあります。最近の分析では、エージェントが2030年までに世界の消費者商取引において3〜5兆ドルを仲介する可能性があると予測されています。これらのエージェントは、人間と同じカタログ、検索結果、商品ページをナビゲートしますが、同じ決定を下すわけではありません。 AIエージェントは、人間のバイアスとは根本的に異なる体系的なバイアスを商品選択にもたらします。構造化され解析可能な属性を過度に重視し、より一貫して価格合理的であり、視覚的または感情的なシグナルを処理できず、基盤となるLLMから継承された位置効果や冗長性効果を示します。その結果、検索クエリのセットが同一であっても、エージェント仲介型ショッピングはカタログ全体で異なる購買分布を生み出し、収益、利益率、需要集中に直接的な影響を及ぼします。 現在、私たちはこれらのバイアスが何であるか、どの程度大きいか、どの商品カテゴリが最も脆弱か、またそれに対して何ができるかについて、体系的な理解を持っていません。このプロジェクトは、エージェントと人間の意思決定の乖離を測定する統制実験を実施し、私たちの管理下にある修正（商品タイトル、説明、構造化属性、推奨シグナル）を通じてエージェントの行動に影響を与えることができる対策をテストすることで、そのギャップを埋めることを提案します。 This fellowship is about studying the implications of agentic shopping. The industry is converging on a near-term future where AI agents increasingly mediate purchase decisions on behalf of consumers. Recent analyses project that agents could mediate $3–5 trillion in global consumer commerce by 2030. These agents will navigate the same catalogs, search results, and product pages that humans do, but they will not make the same decisions. AI agents bring systematic biases to product selection that differ fundamentally from human biases. They over-index on structured and parseable attributes, are more consistently price-rational, cannot process visual or emotional signals, and exhibit position and verbosity effects inherited from the underlying LLMs. The consequence is that even if the set of search queries remains identical, agent-mediated shopping will produce a different distribution of purchases across the catalog, with direct repercussions on revenue, margins, and demand concentration. We currently have no systematic understanding of what these biases are, how large they are, which product categories are most vulnerable, or what we can do about them. This project proposes to fill that gap by running controlled experiments that measure agent-human decision divergence and testing countermeasures that can influence agent behavior through modifications within our control — product titles, descriptions, structured attributes, and recommendation signals. A day in the life チームの多くのメンバーは、午前9時くらいから10時半くらいまでの間に仕事を始め、夕方6時から7時には仕事を終えています。出席が必要なミーティングに参加していれば、勤務時間は自由に決められます。 パートタイムを希望する場合、勤務時間数は採用担当者とともに決定します。フルタイムの場合、労働時間は通常の契約通り週40時間となります。 The majority of the team starts working between 9 and 10.30am until 18-19. You will have complete flexibility to determine your working hours as long as you are present for the meetings where your attendance is required. Number of working hours will be agreed together with the hiring manager in case you want to pursue the Fellowship part-time. In case of full-time, working hours will be 40/week as per a standard contract. About the team 私たちのチームは、日本および世界のすべてのAmazonのベンダー企業に提供されるソリューションを支える製品を発明し、開発しています。私たちは、プロダクトマネージャーやビジネス関係者と協力し、科学的なモデルを開発し、インパクトのあるアプリケーションに繋げることで、Amazonのベンダー企業がより速く成長し、顧客により良いサービスを提供できるようにします。 私たちは、科学者同士のコラボレーションが重要であり、孤立した状態で仕事をしても、幸せなチームにはならないと考えています。私たちは、科学者が専門性を高め、最先端の技術についていけるよう、社内の仕組みを通じて継続的に学ぶことに重きを置いています。私たちの目標は、世界中のAmazonのベンダーソリューションの主要なサイエンスチームとなることです。 Our team invents and develops products powering the solutions offered to all Amazon vendors, in Japan and worldwide. We interact with Product Managers and Business stakeholders to develop rigorous science models that are linked to impactful applications helping Amazon vendors grow faster and better serving their customers. We believe that collaboration between scientists is paramount, and working in isolation does not lead to a happy team. We place strong emphasis on continuous learning through internal mechanisms for our scientists to keep on growing their expertise and keep up with the state of the art. Our goal is to be primary science team for vendor solutions in Amazon, worldwide.

日本の大学で機械学習や関連領域の研究に従事している学生の皆様に向けたフェローシッププログラムのご案内です。Amazon JapanのRetail Scienceチームでは、何百万人もの顧客にインパクトを与える価値あるテクノロジーに繋がるような、新しいプロトタイプやコンセプトを開発するプロジェクトに従事していただく学生を募集しています。プログラムは1ヶ月から3ヶ月の短期間のプロジェクトになります。 プロジェクトの対象となるテーマには、自然言語処理、表現学習、レコメンデーションシステム、因果推論といった領域が含まれますが、これらに限定されるわけではありません。プロジェクトは、チームのシニアサイエンティスト1名または複数名のガイダンスのもとで定義、遂行され、プロジェクト中は他のサイエンティストもメンターとしてフォローします。 学生の皆様が新しいモデルを考案したり、新しいテクノロジーを活用し実験する時間を最大化できるようにすることが目標です。そのため、プロジェクトではエンジニアリングやスケーリングよりも、プロトタイピングを行い具体的に概念実証を行うことに集中します。 また、Amazonでは論文出版も推奨しています。従事した研究開発活動の成果物として出版される論文には著者として参加することになります。 フェローシッププログラムは目黒の東京オフィスで、他のチームと一緒に行われます。Amazonは、プログラム期間中に必要なIT機器（ラップトップなど）、給与と通勤費を支給します。 Are you a current PhD student enrolled in a Japanese university researching Statistics, Machine Learning, Economics, or a related discipline? The Japan Retail Science team is looking for Fellows for short term (1-3 months) projects to develop new prototypes and concepts that can then be translated into meaningful technologies impacting millions of customers. In this position, you will be assigned a project to carry out from areas including but not limited to natural language processing, representation learning, recommender systems, or causal inference. The project will be defined and carried out under the supervision of one or more of our senior scientists, and you will be assigned another scientist as a mentor to follow you during the project. Our goal is to maximize the time you spend on inventing new models and experimenting with new techniques, so the work will concentrate on prototyping and creating a tangible proof of concept, rather than engineering and scaling. Amazon encourages publications, and you will be included as an author of any published manuscript. The fellowship will be carried out from our Tokyo office in Meguro together with the rest of the team. Amazon will provide the necessary IT equipment (laptop, etc.) for the duration of the fellowship, a salary, and commuting expenses. Key job responsibilities このフェローシップは、顧客のデジタルツインの作成に関するものです。私たちは、過去の取引データから販売業者の顧客のデジタルレプリカ（実際の顧客行動に統計的に忠実な合成エージェント）を訓練することを提案します。これらは2つのモードで使用できます。シミュレーションモードでは、数千のツインをインスタンス化し、仮想シナリオ（価格変更、新製品発売、プロモーション戦略）を通じてモンテカルロ方式で実行し、コストのかかる実世界の実験にコミットする前に、信頼区間を持つ集計結果を予測します。対話型フォーカスグループモードでは、各ツインはLLMに基づく自然言語ペルソナによってサポートされ、会話形式でクエリを実行できます。例えば、代表的な顧客になぜ離脱したのか、またはより頻繁に購入するために何が必要かを尋ねることができ、その回答は推測ではなく、その顧客の実際の行動プロファイルと履歴に基づいています。 This fellowship is about the creation of digital twins for customers. We propose training digital replicas of a vendor's customers from historical transaction data — synthetic agents that are statistically faithful to real customer behavior and can be used in two modes. In simulation mode, we instantiate thousands of twins and run them through hypothetical scenarios (pricing changes, new product launches, promotion strategies) in a Monte Carlo fashion to project aggregate outcomes with confidence intervals before committing to costly real-world experiments. In interrogation/focus group mode, each twin is backed by an LLM-grounded natural language persona and can be queried conversationally — for example, asking a representative customer why they churned or what would make them buy more frequently — with responses grounded in that customer's actual behavioral profile and history rather than speculation. A day in the life チームの多くのメンバーは、午前9時くらいから10時半くらいまでの間に仕事を始め、夕方6時から7時には仕事を終えています。出席が必要なミーティングに参加していれば、勤務時間は自由に決められます。 パートタイムを希望する場合、勤務時間数は採用担当者とともに決定します。フルタイムの場合、労働時間は通常の契約通り週40時間となります。 The majority of the team starts working between 9 and 10.30am until 18-19. You will have complete flexibility to determine your working hours as long as you are present for the meetings where your attendance is required. Number of working hours will be agreed together with the hiring manager in case you want to pursue the Fellowship part-time. In case of full-time, working hours will be 40/week as per a standard contract. About the team 私たちのチームは、日本および世界のすべてのAmazonのベンダー企業に提供されるソリューションを支える製品を発明し、開発しています。私たちは、プロダクトマネージャーやビジネス関係者と協力し、科学的なモデルを開発し、インパクトのあるアプリケーションに繋げることで、Amazonのベンダー企業がより速く成長し、顧客により良いサービスを提供できるようにします。 私たちは、科学者同士のコラボレーションが重要であり、孤立した状態で仕事をしても、幸せなチームにはならないと考えています。私たちは、科学者が専門性を高め、最先端の技術についていけるよう、社内の仕組みを通じて継続的に学ぶことに重きを置いています。私たちの目標は、世界中のAmazonのベンダーソリューションの主要なサイエンスチームとなることです。 Our team invents and develops products powering the solutions offered to all Amazon vendors, in Japan and worldwide. We interact with Product Managers and Business stakeholders to develop rigorous science models that are linked to impactful applications helping Amazon vendors grow faster and better serving their customers. We believe that collaboration between scientists is paramount, and working in isolation does not lead to a happy team. We place strong emphasis on continuous learning through internal mechanisms for our scientists to keep on growing their expertise and keep up with the state of the art. Our goal is to be primary science team for vendor solutions in Amazon, worldwide.