OpenAI Software Engineer Interview Questions

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

2.Tell me about the last time you shipped something quickly, learned it had significant issues, and had to rapidly iterate. What was your process for the follow-up improvements?

3.Walk me through a time you took something experimental or research-focused and turned it into a production system. What were the biggest challenges in that transition?

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

5.Give me an example of when your unique perspective or background led you to question a widely accepted technical decision or approach. What was the outcome?

6.Describe the most challenging research prototype or experimental system you've had to make production-ready. What corners did you have to cut, and what did you refuse to compromise on?

7.You're implementing a feature for ChatGPT that needs to handle 50,000 concurrent WebSocket connections. Walk me through your approach to connection management and scaling.

8.Implement a function in Python that batches API requests to optimize GPU utilization. The function should group requests by compatible parameters and handle dynamic batch sizing.

9.We need to implement rate limiting for our API that can distinguish between different types of model requests - some are expensive GPU operations, others are lightweight. How would you design this?

10.You're tasked with building a data pipeline that processes training data for model fine-tuning. The pipeline needs to handle datasets that could be 100GB to 10TB. What's your approach?

11.Design a system that can efficiently store and retrieve billions of text embeddings for similarity search, optimizing for both query speed and storage cost.

12.Design a monitoring system that can detect when our model inference service starts producing lower quality outputs, even when latency and error rates look normal.

13.Tell me about a time you had to convince a skeptical researcher or scientist to adopt an engineering decision that prioritized shipping over perfect optimization. How did you handle their concerns?

14.Tell me about a time you had to lead an urgent response to a production issue that affected millions of users. How did you coordinate across teams while maintaining clear decision-making authority?

15.Describe a situation where you had to lead your team to make a safety-first decision that significantly slowed down development. How did you maintain momentum while ensuring responsible practices?

16.Walk me through a time when you had to quickly ramp up on a completely unfamiliar domain to lead a critical project. How did you build credibility with domain experts while driving execution?

17.Tell me about the most significant technical decision you've influenced at a company level, even though you weren't the most senior person in the room. What was your approach?

18.Describe a time when you had to make a technical leadership decision that balanced OpenAI's rapid iteration culture with the need for responsible AI development. What framework did you use?

19.ChatGPT usage drops 15% on weekends compared to weekdays. We're considering launching a weekend engagement campaign. Estimate the incremental revenue we could capture and whether it's worth the engineering investment.

20.Our GPT-4 API response times increased by 200ms average over the past week, but we haven't changed the model or inference code. Walk me through how you'd diagnose this systematically.

21.We want to estimate how many additional GPU hours we'd need if ChatGPT usage doubled overnight. Walk me through your calculation and what assumptions you'd make about our current infrastructure.

22.DALL-E image generation requests are taking 30% longer during peak hours. You have one week to reduce this. What's your systematic approach to identify the bottleneck and implement a solution?

23.Estimate how much compute cost OpenAI could save annually if we improved our model inference efficiency by 10%. Walk through your assumptions about our current spending and where the savings would come from.

24.We're considering adding real-time web search to ChatGPT, but it would increase response latency by 500-800ms. Estimate how this latency increase would impact user engagement and help me think through whether this feature is worth the tradeoff.