What this interview will probe
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.
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