Sr. Applied Scientist, Ads AI Core Infrastructure
Core
Research and develop novel approaches for agent-data interaction, specifically optimizing real-time access to advertiser data via Model Context Protocol (MCP) servers using generative AI and agentic systems.
Role type
Senior Applied Scientist (Research & Productionization)
Builds
Infrastructure for AI agents to access and reason over real-time advertiser data at scale
Domain
Advertising AI, Agentic Systems, Large Language Model Optimization
Deliverable
production ML models | product features
Required skills
Machine learning model building, Neural deep learning methods, Java/C++/Python programming, Agent orchestration, RAG-based embeddings, Semantic search, Query optimization, Experimentation design
Preferred skills
Large scale distributed systems, Modeling tools (TensorFlow, PyTorch, Spark)
Technologies
MCP servers, CodeAct, RAG, Vector embeddings, Hadoop, Spark
Responsibilities
Research algorithms for agent-data interaction patterns, Invent methods for compressing advertiser context, Design evaluations for data representation impact, Collaborate with engineering to productionize innovations, Author technical papers and file patents, Mentor engineers on ML techniques
Seniority
Senior, hands-on IC with research leadership
