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The Interview Process
Technical Screen
A heavily optimized C++ or Rust coding challenge focusing on low-level memory management, parallel processing, and GPU architecture.
Take-Home Project
Often involves an intense weekend project, such as writing a custom CUDA kernel to accelerate a specific matrix operation.
Onsite Loop
Four to five interviews heavily indexing on high-performance computing (HPC), network scheduling across massive GPU clusters, and hardcore systems engineering.
Real OpenAI Interview Questions
Practice these exact questions faced by previous Software Engineer (Inference / Infrastructure) candidates.
1We are seeing a 200-millisecond latency spike in the API during token generation that only occurs when the context window exceeds 32k tokens. Walk me through your entire debugging trace down to the hardware level. (Excellence / Systems)
2Write a custom CUDA kernel to perform FlashAttention. Explain exactly where the memory bottlenecks will occur on an H100 card. (Hardware / Optimization)
3Tell me about a time you had to scrap months of infrastructure work because the research team suddenly pivoted to a completely different model architecture. How did you handle the whiplash? (Intensity and Scrappiness / Ego-lessness)
4Design a distributed scheduling system that efficiently allocates incoming API requests across 10,000 GPUs running different model sizes (e.g., GPT-3.5 vs. GPT-4). (System Architecture)
5Why do you want to build infrastructure at OpenAI instead of solving similar distributed computing problems at Google or Meta? (Mission Alignment)
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