TLDR Tech / Data
Semantic Search for AI Agents at Scale: Retrieval and Ranking for LinkedIn's Hiring Assistant
LinkedIn built MUSE (Member Understanding Semantic Embeddings) to power semantic search inside Hiring Assistant. It uses a dual-tower Matryoshka embedding model trained on millions of high-quality labels from an LLM Teacher grounded in product policy, combining embedding-based retrieval with a downstream engagement-optimized ranker.
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