DoorDash·Machine Learning Engineer·Onsite - System Design / Architecture
Apr 2026
DoorDash ML engineer system design round focused entirely on building a personalized restaurant recommendation system. Pretty deep dive, covering everything from query parsing to LLM integration, which I wasn't fully expecting.
- Design a personalized restaurant recommendation system where a user enters a query and gets back ranked restaurant results. Walk through query understanding, retrieval, and ranking.
- What real-time signals would you incorporate into the ranking model, and how would you handle serving them at low latency?
- How would you handle cold-start for new restaurants or new users with no historical data?
- How would you incorporate an LLM into this recommendation system? Think about query reformulation, generating explanations, end-to-end ranking, or RAG over reviews and menus.
“This is the kind of question that feels manageable until you're 10 minutes in and realize you've been talking about tokenization for way too long.” The rest of the author's notes on Machine Learning Engineer interview at DoorDash, Onsite - System Design / Architecture round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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