Are these based on real engineering roles?
Yes. The catalog is built around real engineering role patterns so the practice round feels closer to a live interview.
Company practice
OpenAI mock interviews
Pick a role, answer follow-up questions out loud, and get a scored verdict after the interview.
4 rolesPythonC++CUDADistributed Training
Create custom interviewRoles to practice
OpenAI
Research
Research Engineer
Research Engineers design and implement massive-scale distributed machine learning systems, write robust training code, and collaborate with scientists to push frontier models toward capabilities that were previously impossible. The work spans the full loop from algorithm prototyping to running multi-GPU/HPC training jobs reliably at scale. A technical interview would probe distributed training fundamentals (data/model/pipeline parallelism, gradient synchronization), deep PyTorch internals, and the ability to reason about debugging and stabilizing a large training run that has diverged or stalled.
PythonPyTorchDistributed TrainingOpenAI
Security
Security Engineer, Application Security
Application Security Engineers architect and ship production-grade security services — auth services, access brokers, secure proxies, and key-management infrastructure — to reduce risk across OpenAI's products and platform. The role blends strong systems software engineering with isolation, container, and kernel-level hardening expertise. A technical interview would probe threat modeling of a real service, secure design of authn/authz and secrets handling, common web/API vulnerability classes, and reasoning about isolation boundaries for AI agents that execute untrusted code or tool calls.
PythonGoRustOpenAI
ML / AI
Software Engineer, Model Inference
This role builds and optimizes the systems that serve OpenAI's models in production, working alongside researchers to improve inference performance, throughput, and reliability for models powering ChatGPT and the API. Engineers introduce new techniques for low-latency, high-utilization serving of large transformers across GPU fleets. An interview would probe how inference differs from training (KV caching, batching/continuous batching, quantization), GPU memory and latency tradeoffs, and designing a serving stack that maximizes tokens-per-second under tight tail-latency constraints.
PythonC++CUDAOpenAI
ML / AI
Training Performance Engineer
Training Performance Engineers maximize the efficiency, speed, and hardware utilization of OpenAI's large-scale training runs, profiling and eliminating bottlenecks across compute, memory, and the network fabric. They work hands-on with communication libraries (NCCL, MPI, UCX), checkpointing, and large-scale data loading on multi-thousand-GPU clusters. A technical interview would probe GPU architecture and the memory hierarchy, profiling and roofline analysis, collective-communication patterns, and how to diagnose why a distributed run is achieving low Model FLOPs Utilization.
CUDAC++NCCL
ExoForm is not affiliated with OpenAI. This is an independent practice page.
FAQ
Is the interview voice-based?
Yes. ExoForm runs a live voice interview, asks follow-ups, and produces structured feedback after the session.
Can I try it for free?
Yes. You can start with the free interview allowance before upgrading for more practice.