From Tries to Transformers: Scaling Semantic Search

Session Abstract

Netflix search has evolved from simple Trie-based lookups to complex, intent-driven conversational queries. This talk explores our journey to integrate deep Query Understanding and semantic retrieval at scale.

Session Description

This session will cover the architecture and engineering decisions behind modernizing search at Netflix, including:

  • Optimizing Joint-BERT models for real-time inference to achieve low latency response times.
  • Designing a hybrid engine that combines lexical search with both per-field and broad document level embeddings to handle long-tail exploratory queries.
  • Managing the lifecycle of search models, including annotation strategies, labeling consensus, and repeatable benchmark workflows.
  • Real-world results and business impact: Lessons learned from A/B testing, holdback experiments, and their effect on streaming lift and engagement.