Sr. Applied Scientist, Ads AI Core Infrastructure

Description

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 Code Act 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 Code Act 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 product ionize 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…