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.

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

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

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

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

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

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