Careers

Are you driven by innovation and complex problem-solving? At Infrastructure Reliability, we build scalable solutions that ensure the reliability of Amazon's critical systems. Our team develops and operates tools that detect and prevent outages to maintain high availability across Amazon's global infrastructure. Join us to architect solutions that directly impact millions of customers, with the resources and support to make meaningful contributions. The team at Amazon is responsible for building intelligent and real-time insights into service-to-service communications, network traffic, and event correlation across hundreds of Amazon's critical fulfillment and robotics services. Our solutions support visibility into anomalous service behavior to prevent and quickly recover from incidents, ensuring high availability to keep the Customer Promise. We are seeking a talented Senior Applied Scientist to invent the next generation of agentic observability solutions at Amazon scale. In this role, you will define, lead, and build the science behind intelligent systems that reason about complex network and infrastructure telemetry, autonomously detect anomalies, and drive automated remediation. You will own the scientific direction end to end, partnering closely with engineering, product, and Network Development Engineers to translate a long-term science vision into concrete research and delivery roadmaps. Working backwards from the needs of our customers and operations teams, you will take the lead on ambiguous, high-impact problems where neither the problem nor the solution is well defined, and deliver production systems that improve infrastructure availability at scale. You will invent new methods, drive their adoption across multiple teams, and remain deeply hands-on with the hardest technical challenges. Key job responsibilities * Define and own the science vision for agentic observability, translating it into research and engineering roadmaps in partnership with product and engineering leaders. * Build ML and agentic AI systems that autonomously detect, classify, and correlate infrastructure anomalies across network, compute, and service layers at Amazon scale. * Design and develop models for event correlation, root cause analysis, and predictive failure detection using time-series analysis, graph-based methods, and deep learning. * Own the agentic architecture for automated observability workflows, including planning, tool integration, long-horizon reasoning, and multi-agent orchestration. * Define and curate the datasets and evaluation methodologies needed to train, benchmark, and continuously improve detection and classification systems. * Stay deeply hands-on: write production-quality, critical-path code and build core components that take systems from prototype to launch. Partner with Network Development Engineers and operations teams to ground science solutions in real-world infrastructure behavior and operational needs. * Mentor scientists and engineers, raise the science bar, and represent the team in the internal and external scientific community through publications and presentations. A day in the life You will solve real-world problems by analyzing large-scale network telemetry and operational data, designing experiments and simulations, and developing ML models that detect and prevent infrastructure incidents. Your work requires close collaboration with engineers, Network Development Engineers, product managers, and operations leaders across the organization. You will prepare written and verbal presentations to share insights with audiences of varying technical sophistication, and you will iterate rapidly between research and production deployment.

上海职位 - 如果希望在上海工作，请投递本职位。 毕业时间：2026年10月 - 2027年9月之间毕业的应届毕业生 · 投递须知： 1 填写简历申请时，请把必填和非必填项都填写完整。提交简历之后就无法修改了哦！ 2 学校的英文全称请准确填写。中英文对应表，请点击链接查看 https://docs.qq.com/sheet/DVmdaa1BCV0RBbnlR?tab=BB08J2 3 简历不限中英文。 如果您正在攻读自然语言处理（NLP）、信息检索（IR）、机器学习、生成式人工智能或相关方向的硕士或博士学位，并希望将前沿科学研究转化为服务真实客户的产品，我们诚挚邀请您加入亚马逊 International Technology 搜索团队。 我们的目标是帮助亚马逊客户更准确地找到所需商品，并发现符合其需求和兴趣的新商品。您每天的工作都将直接影响全球数百万客户的购物体验。团队使用 TB 级商品、查询和客户行为数据，持续推进搜索、推荐、自然语言理解以及生成式 AI 技术的发展。 在这个岗位中，您将研究并应用 NLP、IR、深度学习、大语言模型（LLM）和基础模型等前沿技术，解决搜索理解、相关性排序、语义匹配、个性化和对话式购物等问题。您将有机会探索预训练、监督微调（SFT）、参数高效微调、检索增强生成（RAG）、提示优化和智能体（Agent）等技术，并针对业务场景建立可靠的离线与在线评估方法。 您将与应用科学家、软件工程师和产品经理密切合作，完成从问题定义、数据分析、算法设计和实验验证，到模型部署、在线测试和持续迭代的完整闭环。您需要根据客户价值和业务目标选择合适的技术方案，并在模型质量、可靠性、安全性、推理延迟和计算成本之间做出合理权衡。 Key job responsibilities Key job responsibilities · 针对 Amazon 搜索和购物体验中的实际问题，提出可验证的科学假设，设计并实现机器学习、NLP、IR 或 LLM 解决方案。 · 使用大规模商品、查询和客户行为数据训练、微调和评估模型，建立可重复的实验与评估流程。 · 探索基础模型在搜索、推荐和对话式购物中的应用，包括 RAG、模型微调、提示优化和 Agent 等方向。 · 设计覆盖相关性、事实性、鲁棒性、安全性、延迟和成本的评估指标，并通过离线实验、A/B 测试和客户反馈验证效果。 · 与工程和产品团队合作，将原型转化为可扩展、可维护的生产系统，并持续分析和改进线上表现。 · 跟踪学术界和工业界的最新进展，形成技术文档，并在适当情况下向内部或外部科学社区分享研究成果。 基本要求 · 正在攻读或已获得计算机科学、计算机工程、机器学习、人工智能、运筹学、统计学或相关领域的硕士或博士学位。 · 具备机器学习或深度学习的基础知识，以及实验设计、统计分析和模型评估经验。 · 具备使用代码和工具实现、训练和评估算法的经验。 · 至少熟练使用一种编程语言，例如 Python、Java 或 C++。 · 了解 NLP、IR、推荐系统或生成式 AI 中至少一个方向的基本方法。 优先条件 · 在 NLP、IR、机器学习、数据挖掘或生成式 AI 相关顶级会议或期刊发表过论文，或有高质量研究项目经历。 · 熟悉 Transformer、LLM 或基础模型，并具有预训练、监督微调（SFT）、参数高效微调、偏好优化或推理优化中的一种或多种实践经验。 · 具有 RAG、向量检索、Embedding、语义匹配、Agent 或工具调用系统的研究或开发经验。 · 熟悉 PyTorch、TensorFlow 等深度学习框架，以及 Hugging Face Transformers 等常用 LLM 工具链。 · 具有搜索引擎或推荐系统经验，尤其是在索引、召回、排序、查询理解、个性化或在线实验方面。 · 具有 LLM 评估经验，能够从相关性、事实性、幻觉、鲁棒性、安全性、延迟和成本等维度衡量系统质量。 · 具有大规模数据处理、分布式训练、模型压缩或高效推理经验。 · 具备良好的批判性思维和技术沟通能力，能够清楚地解释模型选择、实验结果及其局限性，并与跨职能团队合作解决开放性问题。

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.

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

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.

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.

Come join the AWS AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making. About the team Agentic AI drives innovation at the forefront of artificial intelligence, enabling customers to transform their businesses through cutting-edge AI solutions. We build and deliver the foundational AI services that power the future of cloud computing, helping organizations harness the potential of AI to solve their most complex challenges. Join our dynamic team of AI/ML practitioners, applied scientists, software engineers, and solution architects who work backwards from customer needs to create groundbreaking technologies. If you're passionate about shaping the future of AI while making a meaningful impact for customers worldwide, we want to hear from you. 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.

Do you feel the challenge and the adrenaline kick when a huge data-set stares you in the face and you know that somewhere inside are hidden very important business insights that can fundamentally alter the way top business leaders think and act? Do you enjoy presenting strong data backed insights to business leaders; insights that can topple their long held beliefs and compel them to change their direction completely? If yes, then you are the one we are looking for. We are looking to invite passionate leaders, with expertise in generate power business insights from very large datasets, on a journey where the primary aim would be to enable needle moving business impacts through statistical analysis. We are looking for leaders who can envision the design and development of analytical infrastructure which can support strategic and tactical decision-making. Those who join this high visibility team would have to navigate through significant ambiguity in defining business problems and converting them to analytical problems. This role requires additional exposure and experience to Machine Learning. 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, development, evaluate and deploy innovative and highly scalable models for predictive learning • Research and implement novel machine learning and statistical approaches • Work closely with software engineering teams to drive real-time model implementations and new feature creations • Work closely with business owners and operations staff to optimize various business operations • Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation • Mentor other scientists and engineers in the use of ML techniques • Innovate with the latest GenAI technology to build highly automated solutions for efficient customer promotions • Design, develop and deploy end-to-end machine learning solutions in the Amazon production environment to delight Amazon customers • Collaborate with cross-functional teams to develop comprehensive ML/statistical models that can scale to millions of customers to multiple countries About the team Brazil Payments is part of the International Emerging Stores Payments team and focuses on supporting the launch of new payment and financial products to our customers in Brazil.