Careers

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. Please note this position is a Fixed Term Contract role until September 2027. Key job responsibilities - Work backwards from customer problems to research and develop novel machine learning solutions for music and podcast recommendations. Through A/B testing and online experiments done in tandem with engineering teams, you'll implement and validate your ideas and solutions. - Advocate solutions and communicate results, insights and recommendations to stakeholders and partners. - Produce innovative research on recommender systems that shapes the field and meets the high standards of peer-reviewed publications. You'll cement your team's reputation as thought leaders pioneering new recommenders. - Stay current with advancements in the field, adapting latest in literature to build efficient and scalable models A day in the life You will lead innovation in AI/ML to shape Amazon Music experiences for millions. This role will involve developing state of the art models leveraging and advancing the latest developments in machine learning and AI, collaborating with with talented engineers and scientists to guide research and building scalable models across our audio portfolio - music, podcasts, live streaming, and more. You will drive experiments and rapid prototyping, leveraging Amazon's data at scale. This is an opportunity to innovate daily alongside world-class teams to delight customers worldwide through personalization. About the team Our team works with modern AI technologies to transform how customers discover and engage with music and podcasts. We focus on the canonical customer understanding layer for personalization across Amazon audio experiences. We are the "who" experts - we know who the customer is, what they want, where they are in their journey, and how their preferences evolve over time. Amazon Music is available in countries around the world, and our solutions support our mission of delivering music and podcasts to customers in new, exciting ways that enhance their day-to-day lives.

Are you excited about developing agentic AI, LLM and computer vision models that revolutionize Amazon's Fulfillment network? Are you looking for opportunities to apply state-of-the-art AI on real-world problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience — at Amazon scale. To this end, we are looking for an Applied Scientist who will build and deploy models that make smarter decisions on a wide array of multi-modal signals. Together, we will be pushing beyond the state of the art in optimizing one of the most complex systems in the world: Amazon's Fulfillment Network. Key job responsibilities In this role, you will build agentic AI solutions and multi-modal deep learning models that understand how products and packages flowing through Amazon’s fulfillment network. You will build models that solve challenging problems like understanding warehouse operations systems, or visual defect detection on Amazon's entire retail catalog (billions of different items, thousands of new items every day). You will work with a diverse set of very large multi-modal real-world datasets, including imagery, natural language and structured data. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions. A day in the life AFT AI delivers the AI solutions that empower Amazon’s fulfillment network to make smarter decisions. You will work on an interdisciplinary project involving scientists and engineers with deep expertise in developing state-of-the-art AI solutions at scale. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will adapt state-of-the-art agentic AI, deep learning, language understanding and computer vision techniques to develop solutions for business problems in the Amazon Fulfillment Network. About the team Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. AFT AI is spread across NA (Bellevue, WA) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location. Publicly available articles showcasing some of our work: - Visual Defect Detection: https://www.amazon.science/blog/novel-kaputt-dataset-sets-new-benchmark-for-large-scale-visual-defect-detection - Eluna: https://www.aboutamazon.com/news/operations/new-robots-amazon-fulfillment-agentic-ai

Have you ever wanted to solve a mystery or be part of solving a case? Are you fascinated by detective stories or crime shows on TV? Do you love to catch bad actors, build ML models and solve complex problems. If so, working on the WWOS Tech team as a Sr Applied Scientist is the place for you! We detect theft, fraud and organized crime happening across our global supply chain and operations for millions of items, for hundreds of product lines worth billions of dollars of inventory world-wide. We foster new game-changing ideas, creating ever more intelligent and self-learning systems to maximize the cost savings of Amazon's inventory losses. The primary role of a Sr Applied Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on all the fraud investigations happening across Amazon operations. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in ( Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of building fraud detections, detecting organized crime and the ability to use data and research to make changes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Key job responsibilities - Own KPIs that measure theft/fraud management performance and efficiencies. - Detect and automate theft, fraud MOs - Detect organized crime rings and bad actor clusters - Perform end to end evaluation of operational defects, system gaps, and scaling challenges (both system and operational). - Contribute to the overall fraud management and product development strategies. - Present key learnings and vision to stakeholders and leadership. - Integrate ML detection models via software applications About the team We believe that building a culture that is welcoming and inclusive is integral to people doing their best work and is essential to what we can achieve as a company. We actively recruit people from diverse backgrounds to build a supportive and inclusive workplace. Our team puts a high value on work-live balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment.

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 Recommendation and Personalization 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. A day in the life We're using advanced approaches such as foundation models to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external conferences. About the team Prime Video Recommendation Science team owns science solution to power recommendation and personalization experience on various Prime Video surfaces and devices. We work closely with the engineering teams to launch our solutions in production.

职位：Applied scientist 应用科学家实习生 毕业时间：2026年10月 - 2027年7月之间毕业的应届毕业生 · 入职日期：2026年6月及之前 · 实习时间：保证一周实习4-5天全职实习，至少持续3个月 · 工作地点：深圳福田区 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。中英文对应表请查这里（无法浏览请登录后浏览）https://docs.qq.com/sheet/DVmdaa1BCV0RBbnlR?tab=BB08J2 关于职位 Amazon Device &Services Asia团队正在寻找一位充满好奇心、善于沟通的应用科学家实习生，成为连接前沿AI研究与现实世界认知的桥梁。这是一个独特的角色——既需要动手参与机器学习项目，又要接受将复杂AI概念转化为通俗易懂内容的创意挑战。D&S Asia是亚马逊设备与服务业务在亚洲的支柱组织，自2009年支持Kindle制造起步，现已发展为横跨软硬件、AI（Alexa）及智能家居（Ring/Blink）的综合性团队，持续驱动区域业务创新与人才发展。 你将做什么 • 解密AI： 将复杂的技术发现转化为直观的解释、博客文章、教程或互动演示，让非技术背景的业务方和更广泛的社区都能理解 • 技术叙事： 与工程团队协作，以清晰、引人入胜的方式记录AI的能力与局限性 • 知识共享： 协助开发内部工作坊或"AI入门"课程，提升跨职能团队（产品、设计、商务）的AI素养 • 保持前沿： 持续学习并整合最新突破（如大语言模型、扩散模型、智能体），为团队输出简明易懂的趋势简报 • 研究与应用： 参与端到端的应用研究项目，从文献综述到原型开发，涵盖自然语言处理、计算机视觉或多模态AI领域

The Alexa 1P Devices and Marketing organization is looking for an experienced Research Scientist who is energized by the opportunity to help build the future of AI through Alexa+. You will be joining the Alexa Customer Science (ACS) team, whose mission is to surface timely, scientifically-grounded customer insights that help ground Alexa product and marketing decisions. The ideal candidate will have a strong background in quantitative research methods, excellent analytical skills, comfort with ambiguous problem spaces and large-n quantitative and qualitative data sets, and a passion for understanding individual-level attitudes and behavior. Experience with sampling design and survey data weighting is desirable. General AI fluency is a requirement for this role, and experience with AI research workflow integrations is highly desirable. As a Research Scientist with ACS, you will be responsible for designing, conducting, and analyzing research that helps product and marketing teams in Alexa Devices better understand our customers' preferences, experiences, and behaviors. You will own research projects end-to-end, including measurement strategy, data collection, analysis, and reporting to peers and business leaders. You will work closely with cross-functional teams, including product managers, marketing managers, UX researchers and data scientists, designers, and engineers. You should have deep expertise in the design, creation, management, and business use of surveys, randomized experiments (including conjoints, maxdiff, and other choice tasks), and the latest research tools, including those leveraging AI applications. Competency in mixed-methods or qualitative approaches is also desirable. Key job responsibilities - Work closely with product and marketing teams, as well as fellow researchers, to identify research topics and build a research roadmap; communicate and refresh on a regular basis to ensure relevancy. - Create a deep understanding of customers through descriptive, inferential, and experimental approaches (existing or invented) that you identify as being most effective for answering a given business question. - Design, implement, and analyze data from surveys, randomized experiments, and other large-n data sets. - Work with data engineering and business intelligence teams to triangulate survey data with customer engagement and segmentation data. - Create repeatable and scalable mechanisms to measure key customer metrics that drive product iteration. - Synthesize a wide range of primary and secondary data types leading to focused, insightful, and actionable insights that persuade and inspire partners and leaders to take concerted, informed actions. - Work closely with research peers to promote best practices, build resources, and train team members to enable them to execute their own research projects. A day in the life Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. Amazon has ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious and earn trust. About the team The Alexa Customer Science (ACS) team is a team of Research Scientists, UX Researchers, and Market Researchers that surfaces timely, scientifically-grounded insights about customers to guide marketing and product leaders.

The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking an applied scientist with broad interest and expertise in interactive theorem proving, programming language semantics, deductive verification and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities - Work with customer teams to understand the nature of their software and the properties they need to establish of it. - Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. - Use tools spanning from fuzzers to model checkers, and interactive theorem provers to establish program properties. - Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team You will be working with a team of formal verification specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of verified code and tools to accelerate code verification for our customer teams. 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 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. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.

Amazon’s third-party marketplace is a multibillion-dollar global ecosystem, connecting customers and sellers across the world through millions 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 improves 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. For example, measurement of item dimensions (what are the dimensions of a bag of apples?) , large-scale simulation of policy changes (how do marketplace dynamics change when...), Leveraging AI to simplify/document fee policy, resolve disputes, and provide detailed fee explanations to our sellers (explain how this fee is computed and what can be done to reduce costs). As a Senior Applied Scientist on our team, this role will lead the application of machine learning and artificial intelligence to predict and reconcile measurement of products globally. This blends together statistical modeling, application of NLP, image processing, classical machine learning, cost-benefit analysis, causal modeling, and optimization. You will partner closely with engineers and product partners to take your solutions from research to production. You will also help to set the team direction, influence partner teams across product and engineering, help establish a strong scientific culture within the team (e.g., publication, seminars, etc.) and grow junior scientists. 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 mathematical rigor meets real-world complexity, and where your 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 * Identify opportunities to improve Seller Experience and translate ambiguous business challenges into well-defined scientific problems with measurable impact. * Design, develop, and deploy AI/ML models that improve fee accuracy, automate policy-to-code translation, and enhance seller understanding of fee calculations. * Partner closely with engineering and product teams to productionize solutions, meeting latency, scalability, reliability, and other system constraints. * Apply rigorous experimentation, causal inference, and 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. * Build Team Scientific Culture and scientific Standards * Grow and Develop Scientific Talent on the team

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 As a highly experienced and seasoned science leader, you will apply state of the art natural language processing and computer vision research to video centric digital media, while also responsible for creating and maintaining the best environment for applied science in order to recruit, retain and develop top talent. You will lead the research direction for a team of deeply talented applied scientists, creating the roadmaps for forward-looking research and communicate them effectively to senior leadership. You will also hire and develop applied scientists - growing the team to meet the evolving needs of our customers. About the team This team's mission is to deeply understand all content and empower all customers with relevant language options, innovative accessibility assists, and rich title-information across all their content-experiences on Prime Video. We create and publish content on-time that's meaningful, accurate, and accessible to every customer globally. We delight our customers by pushing the boundaries of content understanding and enrichment. Through inclusion and innovation, we do the most fulfilling work of our career.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses, responsible for defining and delivering AI-powered solutions that transform how advertisers make strategic decisions. We deliver billions of ad impressions and process massive volumes of advertiser data every single day. You'll work with us to pioneer breakthrough approaches in how AI agents access and reason over real-time advertiser data at scale. We are using generative AI and agentic systems to help advertising agents provide instant, strategic advice to millions of advertisers. You will need to invent new techniques for agent orchestration, context optimization, and code generation to ensure we're delivering accurate, trustworthy insights with minimal latency and token consumption. You'll create feedback loops to ensure our solutions are constantly evaluating themselves and improving. The Ads Real-Time Data Service team is seeking an exceptional Applied Scientist to research and develop novel approaches for agent-data interaction. The Ads Real-Time Data Service team is solving one of the most critical challenges in advertising AI: instant access to advertiser context. We're building the infrastructure that provides immediate, pre-computed access to advertiser data via Model Context Protocol (MCP) servers—an emerging standard for AI agent-data interaction. We're building summarized data for context using a mix of state of the art techniques like CodeAct and RAG-based embeddings, achieving a fundamental transformation in how AI agents interact with data. This role balances applied research (60%) with productionization (40%), giving you the opportunity to both advance the state of the art and see your innovations deployed at Amazon scale. Key job responsibilities Agent Orchestration & Optimization Research - Research and develop novel algorithms for agent-data interaction patterns that minimize latency, token consumption, and error rates - Investigate multi-agent orchestration strategies for complex advertiser queries requiring data from multiple sources - Develop techniques for automatic query optimization and caching strategies based on agent behavior patterns Large Language Model Context & Token Optimization - Invent new methods for compressing advertiser context representations while preserving semantic meaning and analytical utility - Research optimal metadata generation techniques that help large language models understand and reason over structured advertiser data - Design evaluations to measure the impact of different data representations on agent response quality and token efficiency - Develop adaptive context selection algorithms that dynamically choose relevant data based on query intent RAG-Based Embeddings & Semantic Search - Pioneer new RAG-based embedding approaches optimized for real-time advertiser data delivery with sub-second latency - Research and implement semantic search and retrieval techniques for advertiser datasets using vector embeddings - Design advertiser context frameworks that enable automatic schema mapping from advertiser concepts to data representations - Develop evaluation frameworks to measure performance across dimensions of latency, accuracy, and developer experience Experimentation & Productionization - Design and execute rigorous experiments comparing traditional API orchestration versus CodeAct patterns and RAG-based approaches across metrics like success rate, latency, token consumption, and response quality - Analyze large-scale advertiser interaction data to identify patterns, bottlenecks, and optimization opportunities - Collaborate with engineering teams to productionize research innovations and deploy them to 30+ advertising agents and skills - Establish evaluation metrics and benchmarks for agent-data interaction performance Cross-Functional Collaboration & Thought Leadership - Partner with agent builder teams to understand their data requirements and constraints - Work with platform engineers to implement and optimize MCP servers, data pipelines, and sandbox execution environments - Collaborate with product managers to translate research insights into product features and roadmap priorities - Stay current on latest advancements in agentic AI research, specifically in large language models, multi-agent systems, chain of thought reasoning, and autonomous agents Research Publication & Innovation - Author technical papers for top-tier conferences on agent orchestration, context optimization, RAG-based embeddings, and real-time data integration - File patents for novel techniques in agent-data interaction, token optimization, and CodeAct patterns - Present research findings at internal tech talks and external conferences - Mentor engineers and junior scientists on machine learning techniques, experimental design, and research methodologies A day in the life You start your morning analyzing experiment results from overnight runs comparing three evaluations for different RAG-based embedding approaches. The data shows that one of the embedding pattern is returning a significant improvement in accuracy. You create a spec file with the findings and start drafting a technical paper to be shared with Amazon AI forum. Mid-morning, you're in a design session with the engineering team discussing how to optimize RAG-based embeddings for semantic search over advertiser data. You propose using a hybrid approach combining dense and sparse embeddings to represent campaign metadata, enabling agents to find relevant campaigns through natural language queries while maintaining sub-second latency. You sketch out the architecture and discuss trade-offs between embedding model size, search latency, and accuracy. After lunch, you dive into advertiser interaction logs from advertising agents and skills. You're looking for patterns in how advertisers ask questions about their campaigns. You discover that 60% of queries follow a similar structure: filter campaigns by criteria, aggregate metrics, and compare to benchmarks. This insight leads you to design a new pre-computation strategy using RAG-based embeddings that could reduce query latency by 40%. In the afternoon, you collaborate with an Applied Scientist from an advertising agent team. They're seeing inconsistent results when agents try to calculate complex metrics across multiple campaigns. You investigate and discover the issue is related to how the agent interprets the advertiser context. You propose enriching the RAG-based embeddings with richer metadata descriptions and run experiments showing this improves calculation accuracy from 85% to 98%. Late afternoon, you're prototyping a new approach for adaptive context selection using RAG-based embeddings with the spec file you generated earlier. Instead of providing agents with all available advertiser data, you want to dynamically select the most relevant datasets based on query intent using semantic similarity. You build a quick proof-of-concept and test it on historical queries. The results are promising: 30% reduction in tokens with no loss in response quality. About the team The Ads Real-Time Data Service team is a diverse group of passionate engineers and scientists dedicated to advancing agent-data interaction technology for advertising AI. We value creativity, collaboration, and a commitment to excellence. Our team thrives on tackling complex problems at the intersection of real-time data engineering, AI agent systems, and large language model optimization—turning innovative research ideas into production systems that serve millions of advertisers. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. We have a broad mandate to experiment and innovate, working on problems in agentic AI, context optimization, RAG-based embeddings, and real-time data delivery. We celebrate both research excellence (papers, patents) and engineering impact (production systems serving 30+ advertising agents and skills). We maintain a sustainable pace with flexible work arrangements and a strong focus on work-life balance.