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

What this post added

This post details the engineering effort behind the semantic search strategy for LinkedIn's Hiring Assistant, focusing on the MUSE (Member Understanding Semantic Embeddings) platform. It describes the MUSE Teacher, an LLM-as-a-judge system for generating large-scale relevance labels, and the MUSE embeddings architecture, a dual-tower Siamese model using Matryoshka embeddings for efficient retrieval and ranking. The post highlights the challenges of mapping natural language queries to candidate profiles at scale and the technical solutions implemented, including query understanding, embedding-based retrieval (EBR) using approximate nearest neighbor (ANN) search, and blending with other retrieval strategies for a final re-ranking by an L2 ranker. It also discusses the importance of a shared constitution for defining 'qualified' and the iterative prompt engineering of the MUSE Teacher.