Sr. Applied Scientist, Ads AI Core Infrastructure

Sr. Applied Scientist, Ads AI Core Infrastructure

Job ID: | Amazon.com Services LLC

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 accurate, trustworthy insights with minimal latency and token consumption. You'll create feedback loops to continuously evaluate and improve our solutions.

The Ads Real‑Time Data Service team seeks an exceptional Applied Scientist to research and develop novel approaches for agent‑data interaction. The team is solving one of the most critical challenges in advertising AI: instant access to advertiser context. We build infrastructure that provides immediate, pre‑computed access to advertiser data via the Model Context Protocol (MCP) servers—an emerging standard for AI agent‑data interaction. Summarized data for context is built using a mix of state‑of‑the‑art techniques like CodeAct and RAG‑based embeddings, transforming how AI agents interact with data.

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 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 such as 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, 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.

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.

Basic Qualifications

• 3+ years of building machine learning models for business applications
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc.
• Experience with large scale distributed systems such as Hadoop, Spark, etc.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Benefits: the base salary range for this position is listed below. Your Amazon package will include sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD'D insurance and optional supplemental life plans), EAP, mental health support, medical advice line, flexible spending accounts, adoption and surrogacy reimbursement coverage, 401(k) matching, paid time off, and parental leave. More information is available at

Location: USA, CA, Palo Alto; Salary: $192,200.00–$260,000.00 annually; USA, NY, New York; Salary: $183,800.00–$248,700.00 annually; USA, WA, Seattle; Salary: $167,100.00–$226,100.00 annually.