Design personalized restaurant search and recommendations
Company: DoorDash
Role: Machine Learning Engineer
Category: System Design
Difficulty: medium
Interview Round: Technical Screen
## Scenario
You are designing a DoorDash-like **personalized restaurant recommendation** system.
A user types a free-text query (e.g., “spicy ramen under $20”, “healthy vegetarian lunch”, “best burgers near me”). The system should return a ranked list of restaurants (optionally with dishes) personalized to the user.
## What to design
1. **End-to-end architecture** for query → ranked restaurants.
2. How you would incorporate **LLMs** into the system (where they help, where they shouldn’t be in the critical path).
3. Key **APIs / services**, main **data stores**, and **online vs offline** components.
4. **Ranking approach** (candidate generation + re-ranking), personalization signals, and dealing with constraints (delivery radius, store hours, availability, price, dietary needs).
5. **Latency & reliability** targets and strategies (caching, fallbacks, degradation).
6. **Evaluation**: offline metrics, online A/B metrics, and guardrails.
## Assumptions to clarify
- You have restaurant metadata (location, cuisine, menu items, prices), user history, and real-time availability.
- Traffic is large (e.g., millions of daily users). Typical p95 latency target for search/recs is ~200–500ms.
Quick Answer: This question evaluates a candidate's expertise in designing scalable, personalized restaurant search and recommendation systems, including system architecture, recommendation model design, LLM integration points, API and service boundaries, data storage and online/offline pipeline trade-offs, and operational concerns like latency, reliability, and evaluation metrics. It is commonly asked in system design interviews for machine learning engineering roles to probe practical, application-level skills in recommendation systems and real-time search, emphasizing applied architectural reasoning and engineering trade-offs rather than purely conceptual theory.