How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation
LinkedIn has optimized its AI job search ranking system by implementing a multi-teacher distillation framework. This approach allows the company to train smaller models using larger teacher models, resulting in an 8x speed increase.
Why it matters
This technical advancement demonstrates how large-scale platforms can maintain high-performance AI services while reducing computational overhead.
InfoQ Homepage News How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation
Training a small language model (SLM) to improve relevance and engagement goals, like clicks and applications, requires querying one or more large teacher models for each training example. Serving those teachers can slow down the process. This becomes a bottleneck for a ranking system that must handle hundreds of thousands of queries per second at LinkedIn's scale. Many search and recommendation teams face a common challenge. They struggle to shift from keyword-based systems to unified rankers that are supervised by LLMs.
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