Semantic Search At LinkedIn

• URL: http://arxiv.org/abs/2602.07309v1
• Date: Sat, 07 Feb 2026 01:59:03 GMT
• Title: Semantic Search At LinkedIn
• Abstract summary: We present LinkedIn's LLM-based semantic search framework for AI Job Search and AI People Search.<n>A prefill-oriented inference architecture boosts ranking throughput by over 75x under a fixed latency constraint.<n>One of the first production LLM-based ranking systems with efficiency comparable to traditional approaches.
• Score: 43.73483845395182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present LinkedIn's LLM-based semantic search framework for AI Job Search and AI People Search, combining an LLM relevance judge, embedding-based retrieval, and a compact Small Language Model trained via multi-teacher distillation to jointly optimize relevance and engagement. A prefill-oriented inference architecture co-designed with model pruning, context compression, and text-embedding hybrid interactions boosts ranking throughput by over 75x under a fixed latency constraint while preserving near-teacher-level NDCG, enabling one of the first production LLM-based ranking systems with efficiency comparable to traditional approaches and delivering significant gains in quality and user engagement.

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