---
title: "Semantic Search for AI Agents at Scale: Retrieval and Ranking for LinkedIn’s Hiring Assistant"
url: https://daily.dev/posts/semantic-search-for-ai-agents-at-scale-retrieval-and-ranking-for-linkedin-s-hiring-assistant-ltbv77qsk
source_url: https://www.linkedin.com/blog/engineering/ai/semantic-search-for-ai-agents-at-scale-retrieval-and-ranking-for-linkedins-hiring-assistant
type: article
source: "LinkedIn Engineering"
published: 2026-07-08T19:21:25.706Z
updated: 2026-07-08T19:21:50.480Z
tags: ["vector-search", "embeddings"]
reading_time: 18
upvotes: 0
comments: 0
language: en
---

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

**[LinkedIn Engineering](https://daily.dev/sources/linkedin)** · 18 min read · 0 upvotes · 0 comments

## Summary

LinkedIn's engineering team details how they built MUSE (Member Understanding Semantic Embeddings), the semantic search system powering Hiring Assistant across 1.3B+ member profiles. The system uses a dual-tower Siamese LLM architecture producing Matryoshka embeddings (2048-dim for ANN retrieval, 4096-dim for L2 ranking). A scalable LLM-as-judge teacher system generates millions of qualification-match labels to fine-tune the embedding model with InfoNCE contrastive loss. Infrastructure uses a Lambda architecture with CDC-driven daily delta inference, IVFPQ indexing, and Apache Iceberg tables. Online A/B results show +2.7% highly-relevant candidate rate, +4.1% InMail sends per seat, and +1.8% InMail accepts — with fewer but better candidates surfaced. Key lessons: high-confidence LLM labels outperform human annotators on technical qualifications, contrastive post-training alignment matters more than model size for embedding tasks, and ANN approximation losses compound with post-filter losses.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.linkedin.com/blog/engineering/ai/semantic-search-for-ai-agents-at-scale-retrieval-and-ranking-for-linkedins-hiring-assistant>

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Tags: [#vector-search](https://daily.dev/tags/vector-search), [#embeddings](https://daily.dev/tags/embeddings)

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