This sounds like a standard 'tell me about a project' but it isn't.
Choose a system you truly owned end-to-end and structure your answer as a narrative: problem, constraints, decisions, outcomes. Focus on the 'why' behind your decisions and quantify the impact to show ownership and technical depth.
Pro tip: Emphasize trade-offs and what you learned, not just successes. Interviewers at OpenAI value intellectual honesty and the ability to reason about ambiguity—show how you navigated uncertainty and iterated.
Briefly describe the problem, the system's purpose, and your role. Clarify the constraints (e.g., scale, latency, team size) to frame your decisions.
Walk through 2-3 critical technical decisions you made, the alternatives considered, and why you chose your approach. Highlight trade-offs and how you handled ambiguity.
Summarize how you built and shipped the system, including any major obstacles and how you adapted. Keep it concise but show ownership.
Share measurable results (e.g., performance improvements, cost savings, user impact) and any lessons learned. Be honest about what didn't go as planned.
Briefly reflect on what you'd do differently and how this experience prepares you for challenges at OpenAI. Tie back to the role's focus on system design and adaptability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Anchoring the incident to the same project as the previous question is the tricky part.
Use the STAR method to structure your answer, focusing on the diagnosis process and the specific changes you made. Highlight your technical problem-solving skills and the impact of your fix. Be concise and emphasize the root cause and the trade-offs considered.
Pro tip: Show ownership by mentioning how you prevented similar incidents in the future, and quantify the impact of your fix with metrics like reduced downtime or error rates.
Briefly describe the project and the production incident, including its impact on users or the business. Keep it concise to focus on the diagnosis and fix.
Explain how you identified the root cause: what tools you used (logs, metrics, tracing), how you formed and tested hypotheses, and any collaboration with teammates.
Detail the specific changes you made to resolve the incident, such as code fixes, configuration changes, or infrastructure updates. Mention any trade-offs considered.
Describe how you verified the fix and the measurable impact, such as reduced error rates or improved latency. Include any monitoring or alerts added.
Share what you did to prevent similar incidents, like adding tests, improving documentation, or implementing safeguards. Highlight key takeaways.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.