DoorDash 2025 Summer Intern Projects: GenAI Shopping Engine, Real-Time Slowdown Detection, and Ad Budget Signals
DoorDash faced multiple engineering gaps: the platform struggled to preserve specific user intent in shopping searches, lacked a scalable way to communicate with customers during delivery slowdowns short of full outages, and wasted compute by fetching out-of-budget ad candidates only to discard them at a later filtering stage.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User shopping query input
A user's item search — such as 'fresh vegetarian sushi' — initiates the context shopping engine.
The ad budget signal change produced a 43% drop in search processor latency and a 45% reduction in discarded candidates. The GenAI hybrid shopping engine achieved end-to-end latency of approximately six seconds with store page loads consistently under two seconds. The slowdown detection system reduces customer support volume and improves transparency.
What failed first
For the personalization engine, three single-method approaches were evaluated and found unsuitable: deep neural networks required prohibitive data and resource investment, an LLM-only pipeline had latency exceeding 20 seconds, and embedding-based retrieval alone returned duplicates and lacked nuanced personalization.