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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 technologist, 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! We are looking for a self-motivated, passionate and resourceful Applied Scientist to bring diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. You will spend your time as a hands-on machine learning practitioner and a research leader. You will play a key role on the team, building and guiding machine learning models from the ground up. At the end of the day, you will have the reward of seeing your contributions benefit millions of Amazon.com customers worldwide. Key job responsibilities - Develop AI solutions for various Prime Video Search systems using Deep learning, GenAI, Reinforcement Learning, and optimization methods; - Work closely with engineers and product managers to design, implement and launch AI solutions end-to-end; - Design and conduct offline and online (A/B) experiments to evaluate proposed solutions based on in-depth data analyses; - Effectively communicate technical and non-technical ideas with teammates and stakeholders; - Stay up-to-date with advancements and the latest modeling techniques in the field; - Publish your research findings in top conferences and journals. About the team Prime Video Search Science team owns science solution to power search experience on various devices, from sourcing, relevance, ranking, to name a few. We work closely with the engineering teams to launch our solutions in production.

The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge 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. We are looking for an Applied Scientist III to set the scientific direction for the next generation of agentic AI applications that guide Amazon advertisers. In this role you will define, lead and build the science behind agentic systems that reason, plan, and act autonomously to manage and optimize ad campaigns based on a deep understanding of the advertiser and the marketplace. You will own the agentic architecture end to end, partnering closely with product and engineering leaders to translate a long-term science vision into concrete research and engineering roadmaps. Working backwards from the needs of millions of advertisers, you will take the lead on medium-to-large, ambiguous problems where neither the problem nor the solution is well defined, and deliver customer-facing products that help advertisers create, optimize, and grow their campaigns. You will invent new methods at the product level, and drive their adoption across multiple teams. This role combines science leadership, technical depth, product focus, and business understanding: you will raise the science bar, build consensus on approach across partners, and mentor scientists and engineers while remaining deeply hands-on with the hardest technical problems. Key job responsibilities As an Applied Scientist III on this team you will: - Define the science vision for the agentic campaign management system and, with product and engineering leaders, turn it into delivery roadmaps. - Build agentic systems that autonomously manage and optimize ad campaigns — encoding auction and marketplace dynamics (bidding, budget pacing, keyword and targeting decisions) while balancing advertiser ROI, shopper experience, and marketplace health. - Define and curate the datasets and signals needed to train and evaluate these agents — advertiser and campaign data, auction and bid/budget signals, impressions, clicks, conversions, and search-term/keyword performance. - Stay deeply hands-on: write production-quality, critical-path code and build core components that take agentic systems from prototype to launch. - Own the agentic architecture — planning, tool use and integration (e.g., MCP), long-horizon reasoning (e.g., ReAct, CoT/ToT), and multi-agent orchestration — and stay deeply hands-on, writing production-quality, critical-path code from prototype to launch. - Define the evaluation and safety methodology for agent workflows and drive its adoption as the bar for reliability and trust. - Drive the team's scientific agenda, mentor scientists and engineers, and represent the team in the internal and external scientific community. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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. This team within Sponsored Products and Brands is focused on guiding and supporting millions of advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as autonomously manage campaigns based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.

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 in 2028 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. 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.

Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Senior Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities • Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. • Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. • As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. • Guide technical direction for specific research initiatives, ensuring robust performance in production environments. • Mentor fellow scientists while maintaining strong individual technical contributions. A day in the life • Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure • Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning • Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference • Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems- Drive technical discussions within the team and and key stakeholders • Conduct experiments and prototype new ideas • Mentor team members while maintaining significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

About Amazon Web Services Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 105 Availability Zones within 33 geographic regions, with announced plans for 18 more Availability Zones and 6 more AWS Regions in Malaysia, Mexico, New Zealand, the Kingdom of Saudi Arabia, Thailand. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and consistent support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow. Amazon Web Services came to China in 2013, and has been relentlessly investing and expanding our infrastructure and business since then. Amazon Web Services launched its China (Beijing) Region (operated by Sinnet) in September 2016 and its China (Ningxia) Region (operated by NWCD) in December 2017. In 2019, Amazon Web Services added a new region in Hong Kong, making China the only country with three Amazon Web Services regions aside from the U.S. In 2022, Amazon Web Services launched Local Zone in Taipei. Amazon Web Services has also established an AI lab in Shanghai and two IoT labs in Shenzhen and Taipei. The Amazon Web Services Partner Network has thousands of Partners in China. Amazon Web Services has supported over 10,000 local startups and has provided cloud skills training to over 700,000 talents. Amazon’s first two utility-scale renewable projects—a solar farm and a wind farm—are also generating clean energy to the country’s grid. Amazon Web Services (AWS) is looking for an Applied Scientist to join the Industry Builder Team in China Mainland & Hong Kong (CMHK). The Industry Builder Team designs and builds industry assets and capabilities: reusable solution accelerators, reference architectures, prototypes, and technical building blocks that address the highest-value use cases within a target industry (e.g., games, manufacturing, automotive, retail, media & entertainment). As an Applied Scientist on this team, you are an Industry Builder who owns the AI/ML core of these industry assets. You will invent, implement, and productize generative AI and machine learning components that make our industry assets differentiated, scalable, and ready for customers to adopt. You own the AI portion of the industry asset lifecycle end to end. You do not just prototype: you build assets, take them through review so they are validated for reuse, and you are accountable for the quality, documentation, and adoption of your assets. Because the team is measured by the business impact its assets generate, building assets that are reusable, well-documented, and adopted by the field is a core part of the role. This is a builder role at the intersection of science and industry. You will partner with industry specialists, solutions architects, product teams, and partners to turn industry problems into concrete, deployable AI capabilities, then harden them into assets the field can reuse across many customers. Key job responsibilities • Own the AI/ML and generative AI components of industry assets and capabilities: research, design, implement, and productize state-of-the-art models, agents, and pipelines that power reusable industry solutions • Own the full lifecycle of your assets: build them, take them through review so they are validated for reuse, document them well, and iterate based on field and customer feedback. Keep your assets' documentation and information complete and current so they are discoverable, reusable by the field, and their business impact can be accurately tracked • Translate prioritized industry use cases into technical building blocks (solution accelerators, reference implementations, sample code, and prototypes) that the field and partners can adopt at scale. Collaborate with industry specialists to shape the industry solution roadmap, contributing the science point of view on which AI capabilities are feasible, differentiated, and high-impact • Build rapid prototypes and minimum viable solutions in real customer engagements, then generalize the learnings into reusable assets. Apply generative AI techniques (prompt engineering, RAG, agentic workflows, fine-tuning, model hosting and deployment) and classical ML where appropriate to solve never-before-solved industry problems • Create technical collateral (best-practice guides, tutorials, blog posts, sample code, workshops, and presentations) adapted to technical, business, and executive stakeholders, and evangelize assets to field teams and customers About the team The Industry Builder Team focuses on industry solution innovation. We explore potential solutions and develop them into code assets to help empower our industry customers. Most of our solutions leverage agentic AI technology and are built on top of AWS cloud service building blocks. Diverse Experiences AWS 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. 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 in the cloud.

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.

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 International 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 growth story. Amazon 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 International 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

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。

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.