OpenAI Senior Software Engineer Interview Questions

1.Tell me about a time when your unconventional background or approach helped solve a technical problem that others were struggling with. What made your perspective different?

2.Walk me through a time when you shipped something quickly to get user feedback, then had to significantly change direction based on what you learned. What did you ship and how did you iterate?

3.Describe a time you took an experimental idea or research prototype and turned it into a production system. What were the biggest challenges in that transition?

4.Tell me about a project where you had to work closely with people from completely different disciplines to solve a complex problem. How did you bridge those gaps?

5.Describe a technical decision you made that your team or manager initially disagreed with, but you pushed for anyway. How did you handle that disagreement and what happened?

6.Tell me about the most challenging transition you've made from an experimental or proof-of-concept system to something that could handle real production load. What did you have to completely rebuild?

7.You notice that our model inference service occasionally returns different outputs for identical inputs. This is causing issues for users who expect deterministic behavior. How would you investigate and fix this?

8.Design a monitoring system to track the health and performance of our distributed model training cluster. We're training large models across hundreds of GPUs and need to detect failures quickly.

9.You need to implement rate limiting for the GPT API that's both fair to users and protects our inference infrastructure. Users have different tier limits, some requests are more expensive than others, and we need to handle traffic bursts. How would you design this system?

10.Write a function that efficiently finds similar embeddings for a user's query from a vector database containing millions of document embeddings. Assume you have the embeddings and need to optimize for both speed and relevance.

11.You're designing an API endpoint that needs to handle ChatGPT's traffic - millions of requests per second with varying response times from 100ms to 30+ seconds. How would you architect this to maintain low latency for short responses while not blocking the system during long generations?

12.We're seeing GPU memory fragmentation issues during model training that's causing OOM errors even when total memory usage looks fine. The training job uses multiple A100s with gradient accumulation. Walk me through how you'd debug and fix this.

13.Design the caching layer for ChatGPT's conversation history. Users need instant access to recent conversations, but we're storing billions of messages across hundreds of millions of users.

14.We're launching Sora for video generation and expect it to be extremely popular. Each video generation takes 2-5 minutes on our GPUs. Design a queueing system that can handle millions of requests while giving users reasonable wait time estimates.

15.We want to add image generation to ChatGPT conversations using DALL-E. How would you design the system to handle mixed text and image requests within the same conversation thread?

16.Design a feature flag system for the GPT API that can handle our request volume and needs to make decisions in under 1ms. Some flags need to be evaluated per-user, others per-request, and some need gradual rollouts.

17.GitHub Copilot needs to provide code suggestions within 300ms. Design the inference serving infrastructure that can handle millions of developers typing simultaneously while maintaining that latency target.

18.Design the safety evaluation pipeline that runs before we deploy a new GPT model. It needs to test millions of prompts across different categories and surface potential risks to our safety team within hours.

19.Tell me about a time you convinced a research scientist or ML researcher to change their approach based on production constraints. How did you navigate that conversation?

20.Tell me about a time when you had to help a teammate who was struggling with the intensity or pace at your previous company. What did you do, and how did you know if it was working?

21.Describe a time you had to slow down a team that was moving too fast because you identified a safety or reliability risk they hadn't considered. How did you handle the pushback?

22.Tell me about a time you had to get alignment across multiple teams when each team's immediate priorities seemed to conflict with the broader technical direction. How did you approach it?

23.Walk me through a situation where you recognized that your team's current technical approach couldn't scale to handle the growth you were seeing, but changing it would require convincing leadership to slow down feature development. How did you handle that?

24.Describe a time when you saw someone on your team or a partner team making a technical decision that you believed would have negative consequences for AI safety or alignment. How did you approach that conversation?

25.ChatGPT hit 100 million users in 2 months. Estimate how many GPUs we need to run just the free tier, and walk me through your assumptions about usage patterns and compute requirements.

26.A researcher proposes a new safety technique that could reduce harmful outputs by 90%, but it would slow down all inference by 200ms. How would you structure the decision-making process for whether to implement this?

27.We want to measure the quality of code generated by our models. Design a framework to automatically evaluate code quality across millions of generated samples. What metrics would you track?

28.Our API customers are complaining about inconsistent response times - some requests take 2 seconds, others take 15 seconds for similar inputs. You have access to logs but no obvious smoking gun. Walk me through your debugging approach.

29.Estimate the revenue impact if we reduced GPT-4 API response time from 5 seconds to 1 second average. Consider both direct usage changes and competitive positioning.

30.We're seeing a 3x spike in ChatGPT usage every time we announce a new model. Our current auto-scaling takes 10 minutes to spin up new capacity. Design a predictive scaling system for these announcement-driven traffic surges.