Robinhood·Software Engineer·Onsite - System Design / Architecture·Intermediate

Intermediate

Apr 2026

Summary

System design round at Robinhood for a software engineering role. One question, pretty open-ended, centered around building a restaurant app that estimates wait times given a set of inputs.

Questions Asked (1)

Design a restaurant application that estimates expected customer waiting time based on the number of available waiters, tables, and current customers.

System DesignData ModelingAlgorithms & Data Structures

Author's notes

I spent the first few minutes trying to clarify scope, which I think was the right call.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and assumptions, then model the restaurant as a queueing system with servers (waiters) and resources (tables). Propose a data model and a simple algorithm (e.g., M/M/c queue or simulation) to estimate wait time, and discuss how to handle real-world complexities like table turnover and waiter efficiency.

Pro tip: Emphasize that wait time is not just a function of current customers but also of table turnover rate and waiter availability; propose a feedback loop where the system updates estimates as customers are seated and finish dining.

1. Clarify Requirements and Assumptions

Ask about the scope: is this for a single restaurant or a chain? What data is available (e.g., historical dining times, party sizes)? Define key metrics like average wait time and service rate.

2. Model the System

Represent the restaurant as a queueing system: customers arrive, wait for a table, and are served by waiters. Identify resources: tables (capacity), waiters (servers), and current customers (queue length).

3. Design Data Model and Algorithm

Propose data structures to track tables (e.g., table status, seating capacity), waiters (e.g., current load), and customers (e.g., party size, arrival time). Choose an algorithm: simple heuristic (e.g., wait time = queue length / service rate) or more advanced (e.g., M/M/c queue, discrete-event simulation).

4. Handle Real-World Complexities

Discuss factors like table turnover time, waiter efficiency, party size affecting table assignment, and peak hours. Suggest using historical data to calibrate parameters and update estimates dynamically.

5. Evaluate and Iterate

Propose metrics to evaluate accuracy (e.g., mean absolute error) and a feedback mechanism to improve the model over time. Consider scalability and integration with existing systems.

Key Points to Mention

Queueing theory basics: M/M/c model, arrival rate, service rate, utilization
Data modeling: tables with capacity and status, waiters with current assignments, customer queue with party sizes
Algorithm choices: simple heuristic vs. simulation, trade-offs in complexity and accuracy
Real-world factors: table turnover, waiter efficiency, peak hours, reservations
Dynamic updates: recalculating wait time as events occur (seating, finishing)
Evaluation metrics: accuracy, response time, scalability

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.