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

Senior
Apr 2026

Summary

Uber software engineer interview focused on a deep dive into a past project, covering system design, a tricky integration, a technical decision, and retrospective reflection. The format felt more like a technical debrief than a traditional interview, which I wasn't fully prepared for.

Questions Asked (4)

Q1

Walk me through a large project you worked on. Draw a high-level diagram of the system and explain how data or requests flow through it end to end.

System Design
Author's notes

I'd done this kind of question before but never with an actual diagram component.

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AI HintsAI Generated

Suggested Approach

Choose a project you know deeply and can diagram clearly, focusing on a system with interesting scale or complexity. Start with a high-level diagram, then walk through the end-to-end flow of a typical request, highlighting key components, data stores, and trade-offs. Keep the explanation structured and tailored to Uber's scale and reliability needs.

Pro tip: Emphasize how you handled scale, failures, and trade-offs—Uber cares about systems that work at massive scale with high reliability. Quantify impact (e.g., QPS, latency, cost savings) to demonstrate business awareness.

1. Set the context

Briefly describe the project's goal, your role, and the scale (users, requests per second, data volume). This frames why the system design matters.

2. Draw the high-level diagram

Sketch the main components (clients, load balancers, services, databases, caches, queues) and their connections. Explain each component's responsibility.

3. Trace a request end-to-end

Walk through a typical user action, showing how data flows through the system, including any asynchronous processing, and where state is stored.

4. Discuss trade-offs and challenges

Highlight key design decisions, alternatives considered, and how you addressed scalability, consistency, latency, and failure handling.

5. Summarize impact and learnings

Conclude with the project's outcomes (metrics, business impact) and what you learned or would improve.

Key Points to Mention

  • Scalability: how the system handles growth in traffic and data (e.g., sharding, horizontal scaling).
  • Reliability: fault tolerance, redundancy, and graceful degradation (e.g., retries, circuit breakers).
  • Data consistency and storage choices: SQL vs NoSQL, caching strategies, and CAP trade-offs.
  • Performance: latency optimizations, caching, CDNs, and asynchronous processing.
  • Monitoring and observability: metrics, logging, tracing, and alerting.
  • Trade-offs: explicit decisions made and why, showing engineering judgment.

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

Q2

Describe a complex piece of work from that project, like a non-trivial API integration. How long did it take, and what made it complicated? Be ready to defend your timeline.

API & IntegrationsTechnical Trade-offs
Author's notes

I said two weeks for an API integration and they immediately asked why.

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AI HintsAI Generated

Suggested Approach

Choose a specific API integration that had clear complexity, such as handling rate limits, data consistency, or third-party failures. Structure your answer with context, technical challenges, your actions, and the outcome, explicitly justifying the timeline with concrete milestones and trade-offs. Emphasize how you navigated ambiguity and made decisions to balance speed and quality.

Pro tip: Quantify the complexity and timeline with metrics (e.g., 'handled 10k requests/sec with 99.9% uptime') and acknowledge trade-offs you made, showing you understand engineering economics. Defend your timeline by linking delays to specific technical blockers and the value of resolving them.

1. Set the Context

Briefly describe the project, the API integration's purpose, and why it was non-trivial (e.g., scale, legacy systems, real-time constraints).

2. Detail the Complexity

Explain the specific technical challenges: authentication, rate limiting, data transformation, error handling, or synchronization issues.

3. Outline Your Approach

Describe how you tackled the problem: design decisions, tools used, and how you collaborated with others.

4. Justify the Timeline

Break down the time spent: research, prototyping, implementation, testing, and deployment. Highlight any unexpected obstacles and how you adapted.

5. Share the Outcome and Learnings

Conclude with the results (e.g., performance improvements, business impact) and what you learned or would do differently.

Key Points to Mention

  • Specific technical challenges (e.g., OAuth token refresh, pagination, webhook reliability)
  • Trade-offs made (e.g., consistency vs. availability, build vs. buy)
  • Metrics for success (e.g., latency reduction, error rate decrease)
  • Collaboration and communication with cross-functional teams
  • How you handled failures or unexpected issues during integration
  • Lessons learned and how they apply to future projects

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

Q3

Tell me about a key technical decision you made on that project. What were the options and why did you choose what you did?

Technical Trade-offsSystem Design
Author's notes

This one I actually felt decent about.

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AI HintsAI Generated

Suggested Approach

Pick a decision with clear trade-offs and real consequences, then walk through it as a structured story: context, options, criteria, decision, and outcome. Show that you weighed competing factors like scalability, latency, cost, and maintainability, and that you can articulate why the chosen path was right for the business at that time.

Pro tip: Quantify the impact of your decision (e.g., reduced p99 latency by 40%, cut infra cost by 30%) and briefly mention what you'd revisit today—this signals engineering maturity and self-awareness.

1. Set the context

Briefly describe the project, your role, and the specific problem that required a technical decision. Keep it tight so the interviewer understands the stakes without getting lost in details.

2. Lay out the options

Present 2–3 realistic alternatives you considered, including the one you chose. Explain each option's core idea and its main pros and cons.

3. Define your decision criteria

State the factors that mattered most—e.g., scalability, latency, cost, team expertise, time-to-market—and how you prioritized them given the project's constraints.

4. Explain the decision and trade-offs

Clearly state which option you chose and why it best satisfied your criteria. Acknowledge the trade-offs you accepted and how you mitigated them.

5. Share the outcome and reflection

Describe the results (ideally with metrics) and what you learned. Mention what you might do differently now or how the decision evolved.

Key Points to Mention

  • Scalability and performance implications (e.g., latency, throughput, horizontal vs. vertical scaling)
  • Cost and operational complexity (e.g., infrastructure spend, maintenance burden, on-call load)
  • Team and organizational factors (e.g., existing expertise, hiring, velocity, cross-team dependencies)
  • Data consistency and reliability trade-offs (e.g., strong vs. eventual consistency, failure modes)
  • Time-to-market and reversibility (e.g., build vs. buy, incremental vs. big-bang migration)
  • Measurable impact and lessons learned (e.g., metrics, post-mortem insights, future improvements)

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

Q4

Looking back at that project, what would you do differently?

Technical Trade-offsAdaptability & Ambiguity
Author's notes

Tricky to answer without sounding like you're throwing your old team under the bus.

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AI HintsAI Generated

Suggested Approach

Choose a real project where you made a technical decision that had suboptimal outcomes, and frame your answer around what you learned and how you've applied it since. Be specific about the trade-offs you made at the time and why, then explain what you would do differently now with the benefit of hindsight. Show that you take ownership, are self-aware, and continuously improve.

Pro tip: Avoid saying 'nothing' or blaming external factors; instead, pick a decision that was reasonable given the constraints but could be improved, and emphasize the concrete change you've made in your subsequent work to avoid repeating it.

1. Set the context briefly

Describe the project, your role, and the key decision or approach you took, focusing on the constraints and information available at the time.

2. Identify what you would do differently

Clearly state the specific change you would make, such as a different technology choice, design pattern, or process, and explain why it would have been better.

3. Explain the trade-offs and reasoning

Discuss the trade-offs you considered then versus now, showing that you understand the nuances and can evaluate decisions from multiple angles.

4. Highlight the learning and application

Describe what you learned from the experience and how you've applied that lesson in subsequent projects to demonstrate growth and adaptability.

5. Connect to the role and company

Relate your improved approach to the challenges and values of the target company, showing how you would bring that maturity to their team.

Key Points to Mention

  • A specific technical decision with clear trade-offs (e.g., architecture, database, API design)
  • The constraints and information available at the time that led to the original choice
  • What you would do differently now and why it would be better (e.g., scalability, maintainability, performance)
  • The concrete lesson learned and how you've applied it in later work
  • Self-awareness and ownership without being overly self-critical
  • Alignment with the company's engineering culture and challenges

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