Evolution and Scale of Uber’s Delivery Search Platform

This post details the evolution of Uber's Delivery Search Platform to a semantic search system using Qwen LLMs and a two-tower architecture. It introduces Matryoshka Representation Learning (MRL) for flexible embedding dimension selection, scalar quantization (int7) for cost savings, and optimized ANN parameters (shard-level k) to balance retrieval accuracy with infrastructure costs. It also describes a blue/green deployment strategy at the index column level for safe, automated, and rollback-capable index refreshes, including completeness, backward-compatibility, and correctness validation checks.