OpenAI Staff Software Engineer Interview Questions

1.Tell me about a time when your unconventional background or approach led to a breakthrough on a technical problem that others had been stuck on.

2.Give me an example of when your non-traditional background helped you see a solution that your teammates missed. How did you convince them to try your approach?

3.Walk me through a time when you took an experimental prototype or research idea and had to make it production-ready. What were the biggest challenges in that transition?

4.Tell me about the last time you shipped something quickly, got user feedback, and had to significantly change your approach based on what you learned.

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

6.Describe a time when you had to take cutting-edge research - either your own or someone else's - and turn it into a system that could handle real production workloads and constraints.

7.Write a function that takes a stream of ChatGPT conversation tokens and detects when the model is starting to generate repetitive or looping text. You need to identify this in real-time during generation.

8.Implement a simple rate limiter for the OpenAI API that handles different tiers (free, paid, enterprise) and different types of requests (completions, embeddings, fine-tuning). Focus on the core logic and data structures.

9.ChatGPT's inference API serves hundreds of millions of requests daily. You notice response times are inconsistent - some requests return in 200ms, others take 2 seconds, even for similar prompts. How would you debug this?

10.The GPT API platform serves millions of developer requests per day, but you're seeing intermittent 500 errors that only affect about 0.1% of traffic. Your monitoring shows these errors are distributed across different models and regions. How do you track this down?

11.You're designing the data pipeline that feeds training examples to GPT-5. The pipeline needs to process petabytes of text data, deduplicate it, filter for quality, and serve it to thousands of GPUs during training. What's your architecture?

12.DALL-E generates images that need to be checked for safety violations before showing them to users. You have a safety classifier that takes 50ms per image, but DALL-E can generate 4 images in 200ms. How do you architect this pipeline to minimize user wait time?

13.Design a feature flagging system for ChatGPT that can instantly roll out or roll back model changes to specific user segments. The system needs to handle ChatGPT's traffic volume and support complex targeting rules.

14.GitHub Copilot needs to provide code suggestions in under 300ms while the underlying Codex model runs on shared GPU infrastructure. How would you design the inference serving layer to meet these latency requirements at scale?

15.ChatGPT needs to support function calling where the model can invoke external APIs during a conversation. How would you design the architecture to handle these API calls safely and efficiently at ChatGPT's scale?

16.Design a system to automatically evaluate DALL-E generated images for quality and safety before serving them to users. The system needs to handle DALL-E's generation throughput while maintaining sub-second user response times.

17.You're building a new distributed inference system for Sora video generation. Videos take 2-5 minutes to generate and require coordination across multiple GPU clusters. How do you design this to handle thousands of concurrent requests while managing resource allocation efficiently?

18.The GPT API platform needs to support real-time streaming responses for millions of developers while also handling batch processing jobs that can run for hours. How do you architect the compute layer to serve both workloads efficiently?

19.Tell me about a time when you had to convince a research team to compromise on their ideal approach to make something shippable. How did you navigate that conversation?

20.Tell me about a time when you inherited or joined a team that was struggling technically. What did you identify as the core issues and how did you address them?

21.Describe a situation where you had to make a technical decision that prioritized safety over speed, even when there was pressure to ship faster. What was your reasoning?

22.Walk me through a time when you had to get alignment across multiple teams who had conflicting priorities. How did you build consensus without formal authority over any of them?

23.Describe a situation where you had to advocate for a long-term technical investment that wouldn't pay off for months or years. How did you build support for it?

24.Walk me through a time when you had to make a technical decision that affected the entire company, but different engineering teams disagreed on the approach. How did you navigate that?

25.Estimate how many GPU hours ChatGPT consumes per day globally. Walk me through your assumptions and calculation method.

26.You discover that fine-tuning jobs on the API platform are failing 15% more often this week than last week, but the error messages are generic. How do you investigate this systematically?

27.Estimate the revenue impact if we could reduce ChatGPT's average response time from 2 seconds to 1.5 seconds. What assumptions drive your calculation?

28.DALL-E usage has grown 300% in the past month, but our safety review pipeline is becoming a bottleneck. The current system reviews 100% of images and takes 200ms per image. How do you redesign this to scale without compromising safety?

29.We're launching GPT-4 Turbo, and early adoption metrics show 40% of API developers are trying it, but only 15% are migrating their production traffic from GPT-4. Analyze why this gap exists and how you'd investigate it.

30.Estimate the total cost impact if we gave every ChatGPT Plus subscriber unlimited DALL-E image generations instead of the current monthly limit. Include both direct costs and potential behavioral changes.