LinkedIn's MUSE boosts semantic hiring search, delivering higher recruiter relevance
LinkedIn introduced MUSE, a dual‑tower embedding model trained on millions of recruiter interactions, to power semantic search across 1.3 billion member profiles. The system lets recruiters describe roles in natural language and returns higher‑quality matches, driving measurable gains in candidate relevance and recruiter engagement.