---
title: "Using small language models to serve more relevant DoorDash search ads"
url: https://daily.dev/posts/using-small-language-models-to-serve-more-relevant-doordash-search-ads-tmgus790f
source_url: https://careersatdoordash.com/blog/small-language-models-to-serve-more-relevant-doordash-search-ads
type: article
source: "Doordash"
published: 2026-06-15T22:32:34.409Z
updated: 2026-06-15T22:33:27.976Z
tags: ["nlp", "bert"]
reading_time: 9
upvotes: 0
comments: 0
language: en
---

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# Using small language models to serve more relevant DoorDash search ads

**[Doordash](https://daily.dev/sources/doordash)** · 9 min read · 0 upvotes · 0 comments

## Summary

DoorDash engineering describes how they built a query-item relevance prediction system for search ads using a teacher-student model architecture. A fine-tuned LLM acts as an offline teacher, generating relevance labels (on a 0-1-2 scale) for millions of query-item pairs derived from six months of production traffic. A compact BERT-based bi-encoder (DistilBERT) serves as the student model for low-latency online inference. Embeddings are precomputed and cached; a bilinear layer scores relevance at serving time. The system filters irrelevant items before the ad auction, preventing well-funded but irrelevant campaigns from winning. The SLM-based model achieved a 5.2% relative gain in Precision@2 over the previous gradient-boosted decision tree baseline in A/B testing.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://careersatdoordash.com/blog/small-language-models-to-serve-more-relevant-doordash-search-ads>

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---

Tags: [#nlp](https://daily.dev/tags/nlp), [#bert](https://daily.dev/tags/bert)

[View this post on daily.dev](https://daily.dev/posts/using-small-language-models-to-serve-more-relevant-doordash-search-ads-tmgus790f)