Semantic Search for AI Agents at Scale: Retrieval and Ranking for LinkedIn’s Hiring Assistant

This post details the engineering of the MUSE (Member Understanding Semantic Embeddings) platform for LinkedIn's Hiring Assistant. It describes the MUSE Teacher, an LLM-as-a-judge system for scalable supervision, and the MUSE Embeddings architecture, a dual-tower Siamese model using Matryoshka embeddings. The post highlights the use of 2048-dimensional embeddings for ANN search and the challenges of productionizing billion-scale semantic search, including the query understanding, embedding-based retrieval (EBR), and blending/ranking (L2) layers. It also discusses the evaluation and supervision foundation, including the "Expert Judge" LLM and the MUSE Teacher's prompt engineering for improved accuracy.