Design a Ride Matching Service
Last updated: February 3, 2026
Quick Overview
Design a distributed ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Slack
System Design
Software Engineer
Slack
February 3, 2026Software Engineer
Onsite
System Design
Easy
100
3
73 solved
Design a distributed ride matching system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Slack asks this during the Onsite to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
What the Interviewer Expects
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
Key Topics to Cover
Monitoring and model degradation detection
Data collection and labeling strategy
A/B testing and experimentation
ML objective formulation and metric selection
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- What is your model retraining strategy?
- How would you ensure fairness and reduce bias in the model?
- How would you run A/B tests on different model versions?
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Requirements
- Functional Requirements:
- Match users looking for rides with drivers based on location, destination, and time.
- Allow users to rate their ride experience and provide feedback.
- Provide ...
Capacity Estimation
- Assumptions:
- Average of 1 million active users per day.
- Each user requests a ride approximately 3 times a day.
- Peak hours might see 10x the usual traffic (e.g., 100,000 requests per ...
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