OpenAI DevOps / SRE Interview Questions

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

2.Tell me about the last time you had to quickly deploy a monitoring or infrastructure fix to address a production issue. How did you balance speed with safety?

3.Walk me through a time you took an experimental infrastructure tool or technique and scaled it for production use. What challenges did you hit moving from prototype to serving real traffic?

4.Describe a time you had to work closely with ML researchers or data scientists to solve an infrastructure problem. How did you bridge the gap between their needs and operational requirements?

5.Give me an example of when you challenged a standard DevOps practice or tool choice because you thought there was a better way. How did you approach changing the team's mind?

6.Describe a time you had to operationalize a cutting-edge ML system or experimental infrastructure that had never been run in production before. What unknowns did you have to solve for?

7.You need to implement rate limiting for the OpenAI API that can handle 100K requests per second while being fair across different customer tiers. How would you design this?

8.DALL-E image generation jobs are getting queued up during peak hours, with users waiting 30+ seconds for results. The GPU inference cluster is at capacity but you can't immediately add more hardware. What's your approach?

9.ChatGPT API calls spike from 1M to 50M requests per day, and your inference cluster starts hitting memory limits during peak hours. How would you handle the scaling without disrupting service?

10.Walk me through how you'd set up monitoring and alerting for a system that needs to detect when GPT-4 response quality drops below acceptable thresholds in production.

11.You're tasked with building a CI/CD pipeline for model training runs that need to coordinate across hundreds of NVIDIA DGX nodes. What are the key challenges and how would you architect this?

12.Your team needs to deploy a new safety evaluation system that red-teams every GPT-4 response in real-time before serving it to users. How would you architect this without impacting API latency targets?

13.Design the infrastructure to support Sora video generation where a single request might need 10 minutes of GPU compute time, but users expect sub-30 second feedback on whether their request is feasible.

14.You need to build a model versioning system that lets OpenAI researchers roll back GPT deployments instantly if safety evaluations detect problematic outputs in production.

15.Design a distributed logging system that can capture every token generated by ChatGPT for safety analysis, but also needs to comply with user privacy requirements and handle 50 billion tokens per day.

16.Walk me through how you'd architect a deployment system for OpenAI's API platform that needs to handle model updates across multiple Azure regions while ensuring zero downtime for customers with strict SLA requirements.

17.Design a resource allocation system for OpenAI's GPU clusters that can dynamically prioritize between ChatGPT inference serving, research training jobs, and DALL-E generation requests based on business priorities and real-time demand.

18.You need to design a data pipeline that ingests training data for future GPT models from thousands of sources globally, validates content quality and safety, and needs to process 100TB of text data daily while maintaining data lineage for compliance.

19.Tell me about a time you had to convince a research team to change how they were using infrastructure resources, even though they were skeptical of your recommendations.

20.Tell me about a time when you had to balance conflicting priorities between keeping existing systems stable and supporting experimental AI research that needed infrastructure resources.

21.Describe a time when you identified a potential safety or security risk in production infrastructure that others hadn't noticed. How did you handle it?

22.Walk me through a situation where you had to rapidly scale infrastructure to support a research breakthrough or product launch, but the timeline seemed impossible.

23.Tell me about a time when you had to lead your team through a major infrastructure philosophy change, like moving to containers, adopting new deployment practices, or changing monitoring approaches.

24.Describe a time when you had to advocate upward to senior leadership for a significant infrastructure investment or architectural change that they initially didn't see the value in.

25.ChatGPT usage drops 15% globally on a Tuesday morning with no deployments or incidents. Walk me through how you'd investigate this and what you'd check first.

26.You're designing a capacity planning system for OpenAI's API platform that needs to predict GPU needs 6 months ahead. What data would you use and how would you model this?

27.Estimate how much additional infrastructure cost OpenAI would incur if we doubled ChatGPT's context window from 128K to 256K tokens. Walk me through your assumptions.

28.You notice that 5% of DALL-E image generation requests are timing out during peak hours, but GPU utilization looks normal. How would you debug this?

29.Our safety evaluation pipeline that screens GPT-4 responses is adding 200ms to API response times. The business wants to reduce this to under 50ms. What's your approach?

30.OpenAI wants to launch ChatGPT in a new country where data must stay within national borders. Estimate the infrastructure investment needed and walk through your assumptions.