Careers

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 algorithmic 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 and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the International Emerging Stores organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. About the team Central Machine Learning team works closely with the IES business and engineering teams in building ML solutions that create an impact for Emerging Marketplaces. This is a great opportunity to leverage your machine learning and data mining skills to create a direct impact on millions of consumers and end users.

Passionate about books? The Amazon Books team is looking for a talented Applied Scientist II to help invent, design, and deliver science solutions to make it easier for millions of customers to find the next book they will love. In this role, you will - Be a part of a growing team of scientists, economists, engineers, analysts, and business partners. - Use Amazon’s large-scale computing and data resources to generate deep understandings of our customers and products. - Build highly accurate models (and/or agentic systems) to enhance the book reading & discovery experiences. - Design, implement, and deliver novel solutions to some of Amazon’s oldest problems. Key job responsibilities - Inspect science initiatives across Amazon to identify opportunities for application and scaling within book reading and discovery experiences. - Participate in team design, scoping, and prioritization discussions while mapping business goals to scientific problems and aligning business metrics with technical metrics. - Spearhead the design and implementation of new features through thorough research and collaboration with cross-functional teams. - Initiate the design, development, execution, and implementation of project components with input and guidance from team members. - Work with Software Development Engineers (SDEs) to deliver production-ready solutions that benefit customers and business operations. - Invent, refine, and develop solutions to ensure they meet customer needs and team objectives. - Demonstrate ability to use reasonable assumptions, data analysis, and customer requirements to solve complex problems. - Write secure, stable, testable, and maintainable code with minimal defects while taking full responsibility for your components. - Possess strong understanding of data structures, algorithms, model evaluation techniques, performance optimization, and trade-off analysis. - Follow engineering and scientific method best practices, including design reviews, model validation, and comprehensive testing. - Maintain current knowledge of research trends in your field and apply rigorous scrutiny to results and methodologies. A day in the life In this role, you will address complex Books customer challenges by developing innovative solutions that leverage the advancements in science. Working alongside a talented team of scientists, you will conduct research and execute experiments designed to enhance the Books reading and shopping experience. Your responsibilities will encompass close collaboration with cross-functional partner teams, including engineering, product management, and fellow scientists, to ensure optimal data quality, robust model development, and successful productionization of scientific solutions. Additionally, you will provide mentorship to other scientists, conduct reviews of their work, and contribute to the development of team roadmaps. About the team The team consists of a collaborative group of scientists, product leaders, and dedicated engineering teams. We work with multiple partner teams to leverage our systems to drive a diverse array of customer experiences, owned both by ourselves and others, that enable shoppers to easily find their perfect next read and enable delightful reading experiences that would make Kindle the best place to read.

The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art 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. Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we’re helping businesses grow on Amazon.com and beyond! Key job responsibilities This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: - Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences. - Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life. - Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization. - Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling. - Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team. A day in the life As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced—elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon’s rich data and the world’s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. About the team The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon—such as product detail pages, the homepage, and store-in-store pages—to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack—from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences—all designed to deliver meaningful value.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON WEB SERVICES, INC. Offered Position: Data Scientist III Job Location: Seattle, Washington Job Number: AMZ9674045 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. Position Requirements: Master's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science or a related field and two years of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and five years of progressive post-baccalaureate experience in the job offered or a related occupation as equivalent to the Master's degree and two years of experience. Must have one year of experience in the following skills: (1) building statistical models and machine learning models using large datasets from multiple resources; (2) building complex data analyses by leveraging scripting languages including Python, Java, or related scripting language; and (3) communicating with users, technical teams, and management to collect requirements, evaluate alternatives, and develop processes and tools to support the organization. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $163,909/year to $215,300/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000

Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. The Agentic Customer Experience (ACX) org is responsible for weaving native-AI across the Connect application experiences delivered to end-customers, agents, and managers/supervisors. The Interactive AI Science team, serves as the cornerstone for AI innovation across Amazon Connect, functioning as the sole science team support high impact product including Amazon Q in Connect, Contact Lens and other key initiatives. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLM Agents evaluation and optimization, etc. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow and AWS Sagemaker. The ideal candidate has the ability to understand, implement, innovate on the state-of-the-art Agentic AI based systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. About the team 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 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 (gender 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 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.

MULTIPLE POSITIONS AVAILABLE Employer: AMAZON DATA SERVICES, INC. Offered Position: Data Scientist III Job Location: Seattle, Washington Job Number: AMZ9706811 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product. Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables. Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches. Build consensus on data, metrics, and analysis to drive business and system strategy. Position Requirements: Master's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science or a related field and two years of experience in the job offered or a related occupation. Employer will accept a Bachelor's degree or foreign equivalent degree in Statistics, Applied Mathematics, Economics, Engineering, Computer Science, or a related field and five years of progressive post-baccalaureate experience in the job offered or a related occupation as equivalent to the Master's degree and two years of experience. Must have one year of experience in the following skills: (1) building statistical models and machine learning models using large datasets from multiple resources; (2) building complex data analyses by leveraging scripting languages including Python, Java, or related scripting language; and (3) communicating with users, technical teams, and management to collect requirements, evaluate alternatives, and develop processes and tools to support the organization. Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation. 40 hours / week, 8:00am-5:00pm, Salary Range $165,006/year to $215,300/year. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www.aboutamazon.com/workplace/employee-benefits.#0000

Amazon is seeking a Language Data Scientist to join the Alexa Artificial Intelligence (AI) team as domain expert. This role focuses on expanding analysis and evaluation of speech and interaction data deliverables. The Language Data Scientist is an expert in dialog evaluation processes, working closely with a team of skilled analysts and machine learning scientists and engineers, and is a key member in developing new conventions for relevant annotation workflows. The Language Data Scientist will be asked to handle unique data analysis and research requests that support the training and evaluation of machine learning models and the overall processing of a data collection. Key job responsibilities To be successful in this role, you must have a passion for data, efficiency, and accuracy. Specifically, you will: - Own data analyses for customer-facing features, including launch go/no-go metrics for new features and accuracy metrics for existing features - Handle unique data analysis requests from a range of stakeholders, including quantitative and qualitative analyses to elevate customer experience with speech interfaces - Lead and evaluate changing dialog evaluation conventions, test tooling developments, and pilot processes to support expansion to new data areas - Continuously evaluate workflow tools and processes and offer solutions to ensure they are efficient, high quality, and scalable - Provide expert support for a large and growing team of data analysts - Provide support for ongoing and new data collection efforts as a subject matter expert on conventions and use of the data - Conduct research studies to understand speech and customer-Alexa interactions - Assist scientists, program and product managers, and other stakeholders in defining and validating customer experience metrics

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. The Data, Insights, Science and Optimization, Consumer Product and Tech (DISCO CPT) team is looking for an Data Science Lead to join a team of Data Scientists, Analytics Leads, Business Intelligence Engineers and Data Engineers who analyze big data, provide analytics and insights as well as build models and algorithms that power product for personalized experiences. DISCO MPT team focuses on accelerating Amazon Music customer growth by empowering Product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, science models, self-service analytics, insights and dashboards, deliver engagement and retention at scale. In this role, you will lead the analytics and insights efforts for one of the Amazon Music product teams. You will set the science and analytics roadmap and direction for the domain and work with stakeholders and partners to deliver experimentation insights, authoring white papers and models to advance our analytics and science offerings. The ideal candidate must be willing to effectively lead science solutions, prioritize across multiple stakeholders and projects and be ready to jump into a fast-paced, dynamic and fun environment. Key job responsibilities * A Data Science Lead serving this role will act as thought partner to L8s of Product and Engineering organizations * Lead and drive Data Scientists on strategic growth areas: retention, voice + visual engagement, organization-wide experimentation, Gen AI initiatives, and product excellence. * Bridge science, analytics, and engineering to deliver solutions informing strategic decisions on AM technology investments and long-term business performance * Drive experimentation measurement efficiency tooling, and ROI tracking for AM leadership * Build and maintain customer segmentation models to understand streaming behaviors and its impact on key engagement/retention metrics * Develop structural and predictive models using data science workflows to deliver measurable customer results A day in the life * Lead and mentor data scientists specializing in experimentation and customer analytics * Lead strategic relationships across music search, experimentation, music catalog and FPE initiatives * Conduct experimentation and hypothesis testing, build launch measurements, goal benchmarking, and forecasting * Develop science models for targeting and create operational reporting with anomaly detection for product health monitoring * Partner with product managers to translate business questions into actionable science projects * Influence product roadmaps through data-driven insights on customer behavior patterns * Conduct stakeholder meetings and present insights to senior leadership * Identify and resolve customer experience friction points with cross-functional teams * Drive adoption of best practices and innovative solutions across the organization

Amazon.com’s Buyer Risk Prevention (BRP) mission is to make Amazon the safest and most trusted place worldwide to transact online. Amazon runs one of the most dynamic e-commerce marketplaces in the world, with nearly 2 million sellers worldwide selling hundreds of millions of items in ten countries. BRP safeguards every financial transaction across all Amazon sites. As such, BRP designs and builds the software systems, risk models, and operational processes that minimize risk and maximize trust in Amazon.com. The BRP organization is looking for an Applied Scientist for its Payment Risk Mining ML team, whose mission is to combine advanced machine learning techniques to detect negative customer experiences, improve system effectiveness, and prevent bad debt across Amazon. As an Applied Scientist in Risk Mining, you will be responsible for modeling complex problems, discovering insights, and building risk algorithms that identify opportunities through state-of-the-art techniques including statistical models, deep learning, large language models (LLMs), and agentic AI systems. You will explore and implement state of the art approaches such as graph neural networks, anomaly detection, GenAI-powered investigation agents, and multi-agent systems to automate fraud detection and prevention at scale. You will build and deploy production-ready models and automated systems to improve operational efficiency and reduce bad debt. You will collaborate effectively with business and product leaders within BRP and cross-functional teams to build scalable solutions. The ideal candidate should be able to apply a breadth of tools, and advanced ML techniques, from traditional machine learning to emerging agentic frameworks, to take ideas from experimentation to production, driving tangible improvements in fraud detection and prevention. The candidate should be an effective communicator capable of independently driving issues to resolution and communicating insights to non-technical audiences. This is a high-impact role with goals that directly impact the bottom line of the business. Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies

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.