Interview: How LinkedIn Built AI Job Search Using Large Language Models (LLMs)

Описание: In this episode, I talk with Rohan Rajiv, former Product Lead for LinkedIn’s Job Search and Jobs Marketplace, about how LinkedIn uses large language models (LLMs) to power job discovery and hiring at massive scale.

We unpack how LinkedIn moved from traditional keyword (“lexical”) search to semantic matching — and why that shift fundamentally changed job search quality.

Rohan explains:
• How LinkedIn’s Job Search actually works
• How the employer-side Jobs Marketplace reaches candidates
• Why relevance is so hard with unstructured job data
• How LLMs enabled natural-language job queries
• What an “LLM judge” is — and how it evaluates search quality at scale
• How product policy and evaluation loops improve AI systems
• The real bottlenecks: policy design, judge quality, infra scaling, GPU cost, and latency
• How auctions interact with AI ranking in a two-sided marketplace

We also discuss career skills for AI product builders, why coding fundamentals still matter in the AI era, and Rohan’s 15+ year daily writing practice at ALearningADay.blog.

If you’re building AI products — or curious how LinkedIn’s job search really works under the hood — this is a rare look at LLM systems operating in production at massive scale.

00:00 Introducing Rohan
00:46 What LinkedIn Job Search Does
01:58 The PM Role at LinkedIn
03:31 Listening to User Feedback
04:48 Job Seeker Pain Points
06:44 Why Search Is Hard
09:03 How LLMs Changed Search
12:27 The LLM “Judge”
15:16 How the Judge Works
21:40 Writing Product Policy
24:41 What Policy Really Is
24:52 Query Rules & Attributes
25:16 Measuring Search Quality
26:18 The Evaluation Loop
27:19 Testing & A/B Experiments
29:07 Biggest Bottlenecks
30:41 Scaling Globally
32:26 Search vs Marketplace
36:39 AI Product Career Skills
41:12 Should You Still Learn to Code?
42:32 Daily Writing & Compounding
44:33 Lightning Round

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