Careers

Are you excited about building the science behind marketing measurement for one of the world's largest B2B technology companies? AWS Marketing is looking for an Economist to help expand our measurement framework beyond demand generation — into brand, product-led growth (PLG), and developer programs. You will work to develop the models and analyses that quantify how these investments drive customer acquisition, engagement, and long-term value. In this role, you will contribute to the design and execution of measurement approaches for marketing investments that operate through mechanisms distinct from traditional performance marketing. You will help answer questions such as: What is the incremental impact of brand awareness on customer preferences and conversion costs? How do PLG and developer program investments translate into measurable business outcomes? How should we estimate returns at the program and customer level, not just the channel level? What is the right level of investment across these categories relative to demand generation? This role combines rigorous economics with applied problem-solving. You will build econometric models, run causal analyses, work with large-scale datasets, and translate findings into recommendations for marketing and finance stakeholders. You will operate in a space where established B2B methodology is limited — bringing structure, creativity, and intellectual rigor to novel measurement problems. Key job responsibilities Develop econometric and causal inference models to estimate the impact of brand, PLG, and developer program investments on acquisition, engagement, and revenue Build measurement approaches that produce program-level and customer-level estimates, extending the team's existing channel-level framework Connect upper-funnel metrics (awareness, consideration, salience) and product engagement signals to downstream business outcomes including revenue and customer lifetime value Apply methods suited to long-horizon effects with slow feedback loops — distinguishing these from shorter-cycle performance marketing signals Conduct empirical analyses using large-scale observational and experimental data, ensuring statistical rigor and reproducibility Partner with the senior economist leading this area to design research agendas, scope analyses, and iterate on methodology Communicate findings clearly to technical and non-technical audiences, including marketing leaders and finance partners Stay current on measurement literature across academia, consulting, and industry; bring relevant approaches into AWS's framework Collaborate with data scientists and engineers to operationalize models and integrate results into marketing science products About the team The AWS Marketing Science team is a group of scientists, economists, and engineers building science products that power marketing decisions across AWS. We measure outcomes, target customers, and forecast growth — and we consult with marketing stakeholders on how to optimize their investment. Our team has deep expertise in lower-funnel measurement (multi-touch attribution, incrementality testing, long-term ROI) and is now expanding into brand, PLG, and developer program measurement. You will be part of building that capability from the ground up.

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 subscriptions such as Apple TV+, HBO Max, Peacock, 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 have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. 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. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! 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 About the team Prime Video is pioneering the use of Generative AI to empower the next generation of creatives. Our mission is to make world-class media creation accessible, scalable, and efficient. We are seeking an Applied Scientist to advance the state of the art in Generative AI and to deliver these innovations as production-ready systems at Amazon scale. Your work will give creators unprecedented freedom and control while driving new efficiencies across Prime Video’s global content and marketing pipelines. This is a newly formed team within Prime Video Science!

As an Applied Scientist on the Science SW team, you will collaborate closely with other scientists and engineers to bring Reinforcement Learning (RL) research to production. This role combines the scientific application of ML, and specifically RL and sequential decision making, with software development engineering and a strong product focus. It will be your job to design, implement, and deploy novel RL agents, reward models, and control policies in both prototype and production environments, and to prove their impact in high-fidelity simulation before scaling them across the fleet. Key job responsibilities • Own the research and development of reinforcement learning and sequential decision making solutions spanning deep RL, policy optimization, offline/batch RL, contextual bandits, and multi-agent RL for real-time MHE control and building-wide optimization in a production environment. • Formulate fulfillment operations problems (throughput optimization, flow, merge, and congestion control) as sequential decision-making problems, and design multi-objective reward functions that balance competing operational objectives. • Build and leverage high-fidelity simulation environments for safe offline training, policy validation, and sim-to-real transfer before fleet-scale deployment. • Collaborate across multiple science and engineering teams to integrate RL policies into real-time production and control systems. About the team Amazon is building next generation software, hardware, and processes that will run our global network of fulfillment centers that move millions of units of inventory, and ensure customers get what they want when promised. The Science Software team in the One MHS organization unlocks Material Handling Equipment (MHE) innovation through a multiplicity of disciplines within Artificial Intelligence (AI) and applied science, including Computer Vision (CV), Physics-Informed Neural Networks (PINNs), Optimization, Reinforcement Learning, classical Machine Learning, statistical modeling, and sensing-hardware prototyping. Rooted in first principles aligned experimentation, the team is dedicated to building self-optimizing fulfillment centers, developing the models that drive real-time, building-wide orchestration of MHE. We conduct experiments, develop models, and apply machine learning (ML) at scale to optimize throughput, flow, merge, and congestion control, and to improve operational performance across the fulfillment network.

Are you passionate about solving complex logistics challenges? Our Analytics team is at the forefront of enhancing delivery experiences through data-driven solutions and innovative technology. As a Research Scientist, you will join a team dedicated to optimizing our delivery network, ensuring reliable and efficient service to our customers. We are seeking an enthusiastic, customer-centric professional with strong analytical capabilities to drive impactful projects, implement advanced solutions, and develop scalable processes. In this role, you will have immediate ownership of business-critical challenges and the opportunity to make strategic, data-driven decisions that shape the future of our delivery operations. Your work will directly influence customer experience and operational excellence. The ideal candidate will possess both research science capabilities and program management skills, thriving in an environment that requires independent decision-making and comfort with ambiguity. This role offers the opportunity to make a significant impact on our advanced logistics network while working with pioneering technology and data science applications. Basic qualifications • 3+ years of building machine learning models for business application experience • Knowledge of programming languages such as C/C++, Python, Java or Perl • Experience programming in Java, C++, Python or related language • Experience with neural deep learning methods and machine learning Preferred qualifications: • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field • 3+ years of extensive relevant research experience • Deep expertise in Machine Learning • Proficiency in programming • Core competency in mathematics and statistics • Track record of successful projects in algorithm design and product development • Publications at peer-reviewed conferences or journals • Strategic thinker with good execution skills • Exhibits excellent business judgment • Effective verbal and written communication skills • Experience working with real-world data sets and building scalable models from big data • Experience with modern modeling tools and frameworks such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow • Experience with large scale distributed systems

External job description Job summary Amazon Publisher Services (APS) helps digital publishers around the world build and grow thriving businesses. We provide services and advanced technologies to web, mobile app and advanced TV publishers of all sizes, including many of comScore’s global top 100, to help them monetize their content with demand from multiple programmatic buyers. Our server-side header bidding solutions are fast and reliable across devices, handling billions of queries per day, delivering ads in milliseconds. The result is more profitable advertising for publishers and more relevant ads for customers. As a Data Scientist on this team, you will: • Solve real-world problems by getting and analyzing large amounts of data, diving deep to identify business insights and opportunities, design simulations and experiments, developing statistical and ML models by tailoring to business needs, and collaborating with Scientists, Engineers, BIE's, and Product Managers. • Write code (Python, R, Scala, etc.) to analyze data and build statistical models to solve specific business problems. • Apply statistical and machine learning knowledge to specific business problems and data. • Build decision-making models and propose solution for the business problem you define. • Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. • Analyze historical data to identify trends and support optimal decision making. • Formalize assumptions about how our systems are expected to work, create statistical definition of the outlier, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. • Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. • Conduct written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing. Internal job description The candidate would work with Product, Engineering, BIEs and Scientist across Supply and Demand organization to help make APS the best performing supply path for Amazon ads advertiser customers. They would spearhead efforts to conduct experiments alongside demand and measurement teams to identify optimal perfomance path for advertisers while improving APS Share of Wallet. About the team The Marketplace Services team within Amazon Publisher Services organization primarily focuses on improving monetization for our STV, Web, Mobile and Audio publisher customers. We directly work with 60+ 3p buyers to enable optimal connectivity for publishers to improve their yield. We also own products such as Connections Marketplace (CxM) and Signal IQ that help publishers connect to myriad of 3p and 1p ad tech vendors to boost their bid request quality, while measuring the value of each signal on their bid stream through rigorous A/B testing.

The AWS Central Economics & Science team is looking for a PhD economist. The ideal candidate will be proficient in both reduced form and structural estimation and, most importantly, will be eager to learn new methods where applicable. The ideal candidate should be a problem-solver first, with an ability to bring theoretical frameworks to real-world business problems, working backwards from the business problem rather than from any particular solution method. In this role, you will become a subject-matter expert in cloud infrastructure, creating theoretical frameworks, data-driven insights, and statistical models to help AWS serve its total demand at a lower cost. You will work closely with finance, product, and engineering teams—as well as economists and other scientists—to understand complex systems and products, and will have the freedom to propose, explore, and deliver on a wide variety of projects that emerge from your research. Our team functions like a start-up within the AWS ecosystem—we have the freedom to identify greenfield problems that other economists have not explored yet, and build trust with the business through delivering valuable insights and policy changes. Most importantly, we solve problems at a massive scale and do it in a collaborative, curious, supportive, and fun environment. Key job responsibilities - Become a subject-matter expert in various areas of infrastructure, cost management, and transfer pricing. - Deliver insights that leads to policy changes through analysis, modeling, and theoretical frameworks. - Collaborate closely with non-economist business partners to communicate insights, implement solutions through production models, and develop a research agenda. About the team ACES works on high-impact projects for AWS service teams and leadership. This position will support the cost and transfer pricing optimization team to help AWS continue to scale its infrastructure efficiently in a fast-changing technological and competitive environment.

北京职位 - 如果希望在北京工作，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。

Build AI systems that help Amazon make better sustainability decisions at global scale. Our research questions require more than applying an existing model: they require new scientific methods, trustworthy data foundations, and a path from research hypothesis to production deployment. Sustainability Science and Innovation (SSI) is Amazon's applied research hub for environmental impact. We bring together applied scientists, environmental scientists, economists, and engineers to develop and scale solutions across carbon, water, waste, climate risk, and responsible supply chains—from early hypothesis to production deployment at Amazon scale. SSI is seeking a Senior Applied Scientist to own a research agenda at the intersection of artificial intelligence, data, and sustainability. The role will define the science roadmap, formulate and test hypotheses, establish evaluation standards, and lead solutions from early experimentation through production deployment. Working closely with economists, environmental scientists, engineers, and product leaders, the Senior Applied Scientist will determine which scientific and technical approaches can produce decision-ready results at Amazon scale. The work may include large language models, multimodal models, retrieval-augmented generation, foundation-model adaptation, and other modern machine-learning methods, selected according to the scientific problem rather than applied as ends in themselves. The role will also define how strategic models and datasets are discovered, evaluated, ingested, harmonized, governed, and maintained, because trustworthy AI depends on traceable evidence, stable data contracts, and reproducible evaluation. Applications may include product-level carbon estimation, climate-risk monitoring, and responsible-supply-chain assessment. This role is distinctive because Amazon’s operational scale creates scientific problems that few organizations can study, with unique access to global-scale sustainability data. You'll leverage this unique access to establish scientific methods, governance models, and evaluation standards that can scale across multiple programs. This role shapes not just what problems we solve, but how we solve them rigorously setting a template for AI-driven sustainability science across Amazon's global operations. Candidates do not need prior expertise in sustainability or climate science. The role requires a hands-on scientific leader who can develop rigorous AI and machine-learning methods, work effectively across disciplines, and translate uncertain research questions into measurable, production-ready solutions. Key job responsibilities • Own the research agenda and multi-year science roadmap for AI-enabled sustainability solutions. • Develop and evaluate modern AI and machine-learning methods, including foundation models, multimodal models, retrieval-augmented generation, and model adaptation. • Establish ex ante evaluation criteria, benchmarks, and launch thresholds that distinguish promising prototypes from production-ready methods. • Lead the full scientific lifecycle, from problem formulation and experimentation through production deployment and post-launch measurement. • Define the architecture and governance required to make strategic models and datasets discoverable, traceable, reproducible, and reusable. • Influence senior science, engineering, product, and sustainability stakeholders across organizational boundaries. • Mentor scientists and raise the scientific standard through technical reviews, publications, and reusable methods. About the team Diverse Experiences: World Wide Sustainability values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Inclusive Team Culture: It’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (inclusive diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth: We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance: We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Are you passionate about data, enjoy solving complex analytical problems, leveraging industry leading agentic AI technologies to derive insight at scale - all in a challenging, fast-paced environment? We are seeking an Applied Scientist to accelerate the growth of Amazon Internal Audit’s Data Science & Risk Intelligence initiatives. The team builds ML and AI solutions that expand self-service data utilization by audit teams, utilizing the right methods to derive deeper patterns, and surface insights to gain holistic perspectives while amplifying potential risk mitigation. Key job responsibilities - Work with audit teams, product managers, engineers, and more senior scientists to deliver machine learning and generative AI products that carry real degrees of ambiguity, scale, and complexity. - Design, build, and evaluate agentic AI systems — multi-agent workflows, retrieval-augmented generation, and tool-using agents — that automate and augment audit work, applying rigorous LLM-as-judge and human-aligned evaluation to measure and improve output quality. - Apply statistical analysis and classical machine learning using SQL and scripting languages like Python/R over large datasets to develop insights and recommendations that strengthen internal audit. - Architect secure, scalable solutions on AWS machine learning and generative AI services (e.g., Bedrock, AgentCore, SageMaker), owning the full lifecycle from prototype through production deployment, monitoring, and iterative improvement, working closely with auditors to understand their business needs. - Build and maintain the team's production and experimentation infrastructure, including deployment pipelines, observability and tracing, and evaluation harnesses. - Advance applied research by exploring emerging techniques and sharing findings through internal and external publications, talks, and conferences. A day in the life As an Applied Scientist, you will help shape and execute a product roadmap that connects risk to the business, building AI products — increasingly centered on large language models and agentic systems — that make audit work more effective and efficient. Your work spans the full arc of applied science: framing ambiguous problems, prototyping with the latest generative AI techniques, building rigorous evaluations, and deploying solutions in production. The ideal candidate pairs a strong foundation in data science and machine learning with a builder's instinct for production architecture, thrives on ambiguity, and stays close to a fast-moving research frontier. About the team Internal Audit’s mission is to help our businesses improve controllership, operational efficiency, and customer experience.

The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues