Openai·Software Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

OpenAI SWE interview, project deep dive round that ran about 45 minutes. The interviewer clearly wanted to talk about the hard stuff, not the easy wins.

Questions Asked (1)

Walk me through a technically challenging project you've worked on.

Technical Trade-offsSystem Design

Author's notes

The interviewer steered away from anything that sounded routine pretty fast.

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AI HintsAI Generated

Suggested Approach

Select a project that showcases deep technical complexity and your ability to make trade-offs. Structure your answer using a clear narrative: context, problem, solution, and results. Emphasize the technical challenges, your decision-making process, and the impact of your work.

Pro tip: Quantify the impact of your work (e.g., performance improvements, cost savings) and be prepared to dive deep into any technical detail you mention. Interviewers at OpenAI value depth and the ability to reason about trade-offs.

1. Set the Context

Briefly describe the project, its goals, and your role. Keep it concise to focus on the technical challenge.

2. Define the Challenge

Explain the specific technical problem you faced, why it was hard, and any constraints (e.g., scalability, latency, cost).

3. Describe Your Approach

Walk through your solution, including the design choices, alternatives considered, and trade-offs made. Highlight any innovative or non-obvious techniques.

4. Discuss Implementation

Detail the technical implementation, focusing on the most challenging aspects and how you overcame them. Mention tools, technologies, and collaboration.

5. Share Results and Learnings

Quantify the outcomes (e.g., performance gains, user impact) and reflect on what you learned or would do differently.

Key Points to Mention

The scale and complexity of the problem (e.g., data volume, traffic, distributed systems).
Specific technical trade-offs you made (e.g., consistency vs. availability, latency vs. cost).
Your decision-making process and how you evaluated alternatives.
The technologies and architectures you used (e.g., microservices, databases, ML models).
Quantifiable results and impact (e.g., reduced latency by X%, saved $Y).
Lessons learned and how you applied them to future projects.

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