OpenAI Engineering Manager Interview Questions

1.Tell me about a time when your unconventional background or approach helped your team solve a technical problem that others couldn't crack.

2.Describe a time when you took something from the research or experimental stage and had to make it work reliably for production users. What was your biggest engineering challenge in that process?

3.Walk me through a time when you had to take an experimental or research prototype and turn it into a production system serving real users. What were the biggest challenges in that transition?

4.Tell me about the last time you shipped something quickly to get user feedback, then had to significantly change course based on what you learned. What did you ship first and how did it evolve?

5.Describe a project where you had to collaborate closely with people from completely different disciplines - like researchers, policy experts, or product teams - to solve a complex technical problem.

6.Give me an example of when you had to lead a technical initiative where your team's approach was fundamentally different from industry best practices. How did you navigate that?

7.You're designing the data pipeline for training the next version of GPT. The dataset is 100x larger than what your current system handles. What's your architecture approach?

8.A product manager wants to add real-time collaboration features to ChatGPT where multiple users can edit the same conversation. What technical challenges do you foresee?

9.ChatGPT's API is seeing 500ms P99 latencies during peak traffic. You have 20 minutes before the CEO demo to investors. Walk me through your approach.

10.Your team needs to add a new feature to the GPT API but the current codebase wasn't designed for it. You can either refactor the core architecture or build a workaround. How do you decide?

11.Your team is training a large language model and you're hitting out-of-memory errors on your GPU cluster. The training job costs $100k per run and you're already two weeks behind schedule. How do you debug and fix this?

12.A security researcher reports they can extract training data from your model through carefully crafted prompts. How do you evaluate and respond to this report?

13.Design the inference serving system for ChatGPT to handle 100 million requests per day with sub-200ms latency. How do you architect this for our model sizes?

14.We want to add function calling to DALL-E so users can generate images and immediately edit them through API calls. Design this end-to-end system.

15.GitHub Copilot needs to provide code suggestions in under 100ms while users type. Design the real-time inference system that serves millions of developers simultaneously.

16.Design a system to detect and prevent prompt injection attacks across all OpenAI API endpoints while maintaining sub-50ms overhead per request.

17.We're launching Sora for video generation and expect 10x more compute cost than DALL-E per request. Design the resource allocation and queueing system to serve both premium and free users fairly.

18.Design the evaluation infrastructure to red team new OpenAI models for harmful outputs before release. The system needs to run millions of test prompts and coordinate human reviewers globally.

19.Tell me about a time you had to convince a research team to change their approach because it wouldn't scale to production. How did you bridge that gap?

20.Tell me about the most technically complex project you've led where you weren't the deepest expert in the room. How did you provide value and direction?

21.Describe a situation where you had to slow down a high-performing engineering team because of safety concerns. How did you handle the pushback?

22.Your GPU cluster is down and both ChatGPT inference and model training are affected. You have limited engineering resources. Walk me through how you'd prioritize and lead the response.

23.Describe a time when you had to build alignment between policy, safety research, and engineering teams on a product decision. What was the conflict and how did you resolve it?

24.You're leading the team responsible for GPT-5 training, and three weeks into the run, you discover the training data may have quality issues that could affect model behavior. The training costs $2M per week. How do you decide whether to continue or restart?

25.ChatGPT usage drops 15% on weekends but our compute costs stay roughly the same. How would you approach optimizing our infrastructure to better match this usage pattern?

26.Estimate how many additional GPU hours OpenAI would need if we reduced ChatGPT's response time from 2 seconds to 1 second. Walk me through your reasoning.

27.We're considering making GPT-4 available for free to all users, funded entirely by ads. How would you estimate whether this business model could work?

28.Your engineering team is split between working on GPT-5 training improvements and ChatGPT feature requests from users. Both are important, but you only have bandwidth for one major focus. How do you decide?

29.OpenAI wants to launch a new API endpoint that could handle 10x our current request volume, but we're not sure if demand exists. Design an experiment to test market demand while minimizing infrastructure investment.

30.You're leading a team that needs to build content moderation for Sora video generation, but the safety research team says their detection models won't be ready for six months. Product wants to launch in three months. How do you approach this?