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Openai·Machine Learning Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Interviewed for an ML Engineer role at OpenAI and had to present a technical project I was proud of. Pretty open-ended format, which sounds easy until you're actually in the room trying to decide what counts as 'impressive' to people who work there.

Questions Asked (1)

Q1

Walk us through the most impressive technical project you have worked on.

Technical Trade-offsSystem DesignAdaptability & Ambiguity
Author's notes

I spent way too long deciding which project to pick and ended up going with something technically complex but hard to explain quickly.

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AI HintsAI Generated

Suggested Approach

Choose a project that showcases deep technical complexity, measurable impact, and your ability to navigate ambiguity. Structure your answer to highlight the problem, your approach, key technical decisions, and the results, while emphasizing collaboration and learning.

Pro tip: Quantify the impact of your work (e.g., latency reduction, accuracy improvement, cost savings) and explicitly discuss trade-offs you made, demonstrating engineering maturity.

1. Set the Context

Briefly describe the project's goal, your role, and the team size. Explain why the project was challenging and important.

2. Outline the Technical Approach

Summarize the system design, algorithms, and technologies used. Focus on the most innovative or complex aspects.

3. Highlight Key Decisions and Trade-offs

Discuss critical choices you made, alternatives considered, and why you chose your approach. Mention any constraints (e.g., latency, cost, data).

4. Quantify Results and Impact

Present measurable outcomes (e.g., performance metrics, business impact) and how they were validated.

5. Reflect on Learnings and Adaptability

Share what you learned, how you handled setbacks or ambiguity, and how the experience improved your skills.

Key Points to Mention

  • Problem complexity and scale (e.g., data volume, model size, real-time constraints)
  • Technical stack and architecture (e.g., distributed training, model serving, monitoring)
  • Trade-offs made (e.g., accuracy vs. latency, cost vs. performance)
  • Quantifiable results (e.g., improved accuracy by X%, reduced latency by Y ms)
  • Collaboration and cross-functional work (e.g., with researchers, product managers)
  • Adaptability to changes or unexpected challenges during the project

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