Sr. Applied Scientist, Ads AI Core Infrastructure

About the role

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.

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

Qualifications

• 3+ years of building machine learning models for business application experience
• 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.