Careers

Are you a data enthusiast? Are you a creative big thinker who is passionate about using data and optimization tools to direct decision making and solve complex and large-scale challenges? If so, then this position is for you! We are looking for a motivated individual with strong analytic and communication skills to join the effort in evolving the fulfillment center network of tomorrow. At Amazon Worldwide Fulfillment Design and Engineering, we are designing the future and if you are in quest of an iterative fast-paced environment, where you can drive innovation through data visualization products, advance analytics and machine learning at scale, this is your opportunity. In this role, your main focus will be to analyze fulfillment network trends, identify business opportunities, provide project direction, and communicate design and technical requirements within the team and across stakeholder groups. You will own the science vision, lead AI/ML research strategy and roadmap for the org, drive metrics, assist science groups in initial solution design and audit new flow implementations. A successful candidate in this position will have a background in communicating across significant differences, prioritizing competing requests, and quantifying decisions made. Key job responsibilities - Analyze, model and interpret data for Amazon warehouse operational performance. - Predictive analysis using Machine Learning models at scale. - Partner with other scientists and engineers to translate frontier AI research into production-grade tools for process design developments. - Data analysis, modeling, network flow prediction using Excel, Pivot tables, VBA, Tableau, SQL (Amazon Redshift), R, Python. - Innovate and simplify data solutions to standardize and enable automation. - Support Worldwide Engineering teams by providing necessary data for of process flows and selection of various MHE's. - Support process improvement initiatives among site operations, engineering, and corporate systems groups by managing data pipelines. - Develop data and optimization-based solutions to the best-in-class process flow to improve the throughput of the fulfillment facilities. A day in the life Amazon offers a full range of benefits for you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Amazon Web Services is looking for world class scientists to join the Security Analytics and AI Research team within AWS Security Services. This group is entrusted with researching and developing core ML and AI solutions for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Security Hub (https://aws.amazon.com/security-hub/). In this group, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale ML/AI problems and/or agentic systems, this will be an exciting opportunity. The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services. Our team also puts a high value on work-life balance. We thrive to provide a healthy balance between your personal and professional life which is crucial to your happiness and success here. Key job responsibilities - Invent, implement, and deploy state of the art ML/AI algorithms and systems for information security applications. - Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment. - Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services. - Report results in a scientifically rigorous way. - Interact with security engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for.

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.

Amazon Ads Brand Safety & Suitability protects advertisers from exposure to unsafe, unsuitable, or policy-violating content across web, mobile app, CTV, and audio advertising inventory. Our mission is to ensure that every ad impression delivered through Amazon's demand-side platform appears adjacent to content that meets advertiser trust expectations while giving brands granular controls to define suitability on their own terms. We operate at the intersection of advertiser trust, publisher quality, and supply integrity. AI is fundamentally changing the content landscape. Content is now generated at unprecedented scale — faster, cheaper, and increasingly sophisticated. Low-quality, deceptive, AI-generated, and synthetic content evolves in real time, constantly adapting to evade detection. The volume and velocity of new content entering the advertising system has outpaced traditional classification approaches. We are looking for an Applied Scientist to work on the next generation of AI-powered Brand Safety and Content Classification systems designed to protect advertisers and elevate supply quality at internet scale. This is not a traditional classification problem. You will build systems that make millisecond-level decisions across billions of content signals while continuously adapting to emerging content risks driven by generative AI. You will own the science strategy for LLM-powered classification and semantic understanding, real-time multimodal content evaluation, adversarial ML and adaptive model resilience, proactive risk intelligence and content risk hunting, AI-generated and synthetic content detection, and large-scale abusive content system identification and disruption. You will define how modern AI separates high-quality advertising inventory from unsafe, unsuitable, and policy-violating content — across web, mobile app, CTV, and audio surfaces. What Makes This Role Unique Generative AI has dramatically lowered the cost of producing deceptive, policy-evasive content, and the adversary evolves daily. Your detection systems must reason contextually, adapt rapidly, and generalize beyond previously seen content risk patterns. Static models fail here; you will build living systems that learn and respond in real time. You will do this at internet scale, developing low-latency ML and LLM-powered systems evaluating content safety, brand suitability, misinformation risk, and emerging content risk vectors across massive real-time traffic streams, making billions of decisions per day with single-digit millisecond latency constraints. This role sits at the intersection of frontier AI research and large-scale production engineering, combining deep science, system-wide impact, and business-critical outcomes. The models your team ships directly influence billions of dollars in advertising spend and the trust of the world's largest brands in Amazon DSP. The Science Problems Are Genuinely Hard You will tackle challenges including detecting sophisticated AI-generated and synthetic content, understanding nuanced contextual brand risk, identifying coordinated MFA space before they scale, balancing precision, recall, latency, explainability, and fairness, designing adaptive models resilient to adversarial evolution, and leveraging LLMs for semantic understanding in real-time, latency-constrained environments. Why This Matters Few roles offer the opportunity to work at the intersection of frontier AI, internet-scale production systems, adversarial environments, and business-critical impact — while tackling open-ended scientific challenges with real-world societal relevance. As AI reshapes the internet, the systems your team builds will define what trustworthy, high-quality digital systems look like for the next decade. Key job responsibilities - Own the science strategy for AI-powered brand safety classification. - Build LLM-powered content classification systems making billions of decisions/day at single-digit millisecond latency - Develop multimodal evaluation pipelines reasoning across text, images, audio, and video in real time - Design adaptive ML systems resilient to adversarial evolution, semantic understanding for nuanced contextual brand risk. - Define measurement frameworks and drive continuous improvement - Translate research into production — own the path from prototype to deployed model - Publish at peer-reviewed venues; contribute to the scientific community in adversarial ML, NLP, and content safety - Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems.

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. * Define and implement new automated reasoning features that employ scalable and efficient approaches to solve complex problems using neural learning and symbolic/formal reasoning * Apply software engineering best practices to ensure a high standard of quality for all team deliverables * Work in an agile, startup-like development environment * Deliver high-quality scientific artifacts * Work with the team to help drive business decisions About the team Why AWS? 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 Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon 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 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 in the cloud.

Amazon’s operations in Australia is at a unique phase of rapid expansion. As our selection and local fulfilment network grows, the complexity of managing supply chain increases. To systemically address these complexities, we are establishing a team of subject matter experts by expanding Supply Chain Optimisation Technology (SCOT) team presence to Australia. We are looking for an exceptional Data Scientist to join this specialised team and help build the analytical foundations that allow us to automate and optimise our local supply chain at scale. Key job responsibilities - Build Predictive Models: Design, develop, and deploy machine learning models (e.g., time-series forecasting, regression, classification) to predict inbound volumes, leveraging signals from demand forecasts, vendor behaviour, and upstream planning systems unique to the Australian supply chain. - Drive Root-Cause Analysis: Apply statistical methods and causal inference techniques to quantify defect attributions across plan-over-plan changes, actuals-over-plan variances, and forecast accuracy degradation, translating complex analytical findings into actionable insights for stakeholders. - Enable Automated Intelligence: Leverage agentic workflows and LLM-based pipelines to build self-improving prediction systems for inbound volumes, automating feature engineering, model retraining, and anomaly detection to replace manual heuristics. - Advance Experimentation: Design and execute A/B tests and counterfactual analyses to measure the impact of supply chain interventions (e.g., buying policy changes, capacity adjustments) on inbound volume outcomes, providing rigorous evidence for decision-making. - Influence Strategy: Synthesise insights across product demand forecasting accuracy, inventory efficiency, and capacity planning to build data-driven narratives that influence inbound volume projections and supply chain strategy at the leadership level. About the team Have you ever ordered a product on Amazon and wondered how it got to you so fast? Wondered where it came from and how much it cost? If so, Amazon's Supply Chain Optimisation Technology (SCOT) organisation is for you. At SCOT, we solve deep technical problems and build innovative solutions in a fast-paced environment. Learn more about SCOT: http://bit.ly/amazon-scot.

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

The Prime Video Science team leverages the latest in machine learning and AI techniques combined with causal inference to bring scientific rigor to the biggest decisions in entertainment: what content to make, what to license, and where to invest. We build large-scale models that simulate how our global customer base responds to change, and we get to see that work shape what the business does. Prime Video is an industry-leading entertainment business and a critical driver of Amazon Prime subscriptions, contributing to customer loyalty and lifetime value. We're looking for a Data Scientist to help us design the experiments that ground our models in reality, evaluate the AI systems we build, and turn our model and experiment results into insights the business can act on. As a Data Scientist on this team, you will design and analyze experiments, build evaluations for AI systems, and run deep-dive analyses on our models, experiments, and customer data. You will work close to science: probing why a model behaves the way it does, pressure-testing results before they reach senior leaders, and, where it helps, building your own statistical and causal models. The candidate should have strong communication skills and the ability to translate complex, ambiguous analyses into clear findings for both technical and business audiences. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail and the ability to work in a fast-paced and ever-changing environment. Key job responsibilities • Design and analyze randomized experiments that validate and calibrate our models and measure the impact of content and product changes. • Build evaluations and benchmarks for the AI systems the team develops and define what "good" looks like for them. • Run deep-dive analyses on model outputs, experiment results, and customer behavior to surface the story behind the numbers and catch issues before they reach stakeholders. • Apply statistical modeling, causal inference, and data analysis to answer business questions and inform major investment decisions. • Communicate findings to business, finance, engineering, and science stakeholders through clear written analyses and business-facing documents. About the team The Prime Video Science team is a multidisciplinary group of applied scientists, data scientists, economists, and engineers. We take on some of the hardest research questions in the business, and our work carries visibility up to the CFO/CEO level. We pursue ambitious research at the intersection of machine learning, AI, and causal inference, turning that research into innovations that improve customer experience and strengthen business profitability. Few science teams get to work on problems this hard and this impactful; if that combination excites you, we'd love to talk.

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.