Scaling Multilingual Semantic Search in Uber Eats Delivery

TL;DR

This paper describes a production-scale multilingual semantic search system for Uber Eats that unifies retrieval across various categories, leveraging large-scale training and innovative embedding techniques to improve recall.

Contribution

It introduces a novel fine-tuned Qwen2 two-tower model with multi-vertical embedding capabilities and practical insights for large-scale multilingual retrieval systems.

Findings

Achieved significant recall improvements over baseline in six markets.

Demonstrated effective multi-vertical retrieval with a unified model.

Provided practical lessons for deploying large-scale semantic search systems.

Abstract

We present a production-oriented semantic retrieval system for Uber Eats that unifies retrieval across stores, dishes, and grocery/retail items. Our approach fine-tunes a Qwen2 two-tower base model using hundreds of millions of query-document interactions that were aggregated and anonymized pretraining. We train the model with a combination of InfoNCE on in-batch negatives and triplet-NCE loss on hard negatives, and we leverage Matryoshka Representation Learning (MRL) to serve multiple embedding sizes from a single model. Our system achieves substantial recall gains over a strong baseline across six markets and three verticals. This paper presents the end to end work including data curation, model architecture, large-scale training, and evaluation. We also share key insights and practical lessons for building a unified, multilingual, and multi-vertical retrieval system for consumer search.