Design a Store Recommendation System and Explain ML Trade-offs
Company: DoorDash
Role: Software Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
Design a store recommendation system and discuss ML domain trade-offs, including a decision-tree-style refund model.
```hint Hint 1
Start by stating assumptions, then work from requirements to trade-offs and validation.
```
```hint Hint 2
Use concrete examples from the prompt and make edge cases explicit.
```
### Constraints & Assumptions
- Preserve the source scope; do not assume extra company-specific systems.
- Focus on interview reasoning, correctness, and operational trade-offs.
- Explain how you would validate the answer with examples, metrics, or tests.
### Clarifying Questions to Ask
- What exact user, system, or business goal should this solve?
- What scale, latency, reliability, or privacy constraint matters most?
- What existing infrastructure or code must the solution integrate with?
- What output or behavior will the interviewer use to judge success?
### What a Strong Answer Covers
```premium-lock What a Strong Answer Covers
```
### Follow-up Questions
- How would your answer change at 10x scale?
- What would you monitor in production?
- What edge case is easiest to miss?
- What would you simplify if this were a 60-minute implementation round?
Quick Answer: Practice a DoorDash machine learning interview question about design a store recommendation system and explain ml trade-offs. The prompt covers objective framing, data assumptions, evaluation, trade-offs, and production risks without giving away the answer.