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.
Role practice
Research interview practice
Pick a role, answer follow-up questions out loud, and get a scored verdict after the interview.
3 rolesPyTorchDistributed TrainingPythonJAX
Create custom interviewRoles to practice
Anthropic
Research
Research Engineer, Interpretability
The Interpretability team reverse-engineers the internal computations of large language models — studying features, circuits, and superposition — to make Claude's behavior understandable and steerable. Research Engineers build tooling and run experiments that turn mechanistic hypotheses into measurable results on frontier models. A technical interview would probe transformer internals (attention, residual stream, MLP layers), designing experiments to isolate and validate a learned feature or circuit, and writing efficient code to extract and analyze activations at scale.
PythonPyTorchJAXCerebras
Research
Applied Machine Learning Research Scientist
Adapts and optimizes large language and vision models for efficient training and inference on Cerebras systems, researching architectures, sparsity, and numerical techniques that exploit wafer-scale compute. Publishes and partners with engineering to bring research into the production model stack. A technical interview would probe transformer internals, training-at-scale tradeoffs (parallelism, memory, precision), and the math behind an optimization or sparsity method you would propose.
PyTorchLLMsDistributed TrainingOpenAI
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 Training
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.