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Uber·Software Engineer·Onsite - System Design / Architecture·Senior

Senior
May 2026

Summary

Third round VO at Uber for a software engineering role, system design focused on Uber Eats. Pretty standard setup but scoping the problem early seemed to matter a lot here.

Questions Asked (1)

Q1

Design the Uber Eats platform (or a core component of it).

System DesignTechnical Trade-offsData Modeling
Author's notes

First thing to do is nail down what part you're actually designing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and requirements with the interviewer, then focus on a core component like order matching or delivery tracking. Design a scalable, fault-tolerant system using microservices, and discuss trade-offs in data consistency, latency, and availability.

Pro tip: Proactively discuss how you would handle peak load and failures, as Uber values reliability and scalability. Mention real-world constraints like geo-distribution and eventual consistency to show practical experience.

1. Clarify Requirements

Ask questions to understand functional and non-functional requirements, such as scale, latency, consistency needs, and key features (e.g., order placement, tracking, payments).

2. High-Level Design

Sketch the main components (e.g., API gateway, services, databases, message queues) and how they interact. Choose a core component to deep dive if time is limited.

3. Deep Dive into Core Component

Detail the design of a critical part, such as order matching or delivery tracking, including data models, algorithms, and scaling strategies.

4. Address Scalability and Reliability

Explain how the system handles high traffic, failures, and data consistency (e.g., sharding, replication, caching, circuit breakers).

5. Discuss Trade-offs and Bottlenecks

Analyze trade-offs (e.g., SQL vs NoSQL, strong vs eventual consistency) and identify potential bottlenecks and mitigation strategies.

Key Points to Mention

  • Microservices architecture for modularity and independent scaling
  • Geospatial indexing (e.g., geohash, Quadtree) for efficient matching of drivers and restaurants
  • Event-driven architecture with message queues (e.g., Kafka) for asynchronous processing
  • Data consistency models (e.g., eventual consistency for order status, strong consistency for payments)
  • Caching strategies (e.g., Redis) to reduce latency for read-heavy operations
  • Fault tolerance and redundancy (e.g., multi-region deployment, retries, idempotency)

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