Reimagining LinkedIn’s search tech stack

This post details the engineering effort behind LinkedIn's semantic search stack, which leverages LLMs for a more intuitive and personalized search experience. It describes the high-level infrastructure involving query understanding, embedding-based retrieval (EBR) on GPUs, and a Cross-Encoder SLM for ranking. Key optimizations include score caching and a ranking-depth controller. The post also elaborates on the process of measuring relevance quality using LLM judges, including defining product policies, training LLM judges via distillation, and continuous quality measurement. It provides technical details on EBR relevance modeling, including the dual-tower architecture, contrastive InfoNCE loss, margin-based ranking loss, and the use of hard positives and negatives for training.