Turn frontier ML research ideas into working systems inside Cohere's open research arm

Cohere Labs is hiring a hands-on Research Engineer to build experiments, debug models, scale training pipelines, and implement new methods alongside scientists. This is a practical engineering role — not a pure research seat — that expects fluency with PyTorch, distributed training, and evaluation/finetuning workflows, with room to grow research instincts on the job.

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What this interview tests

Hands-on ML engineering: building and debugging experiments, training pipelines
PyTorch fluency and distributed training tradeoffs
Finetuning, RLHF, and evaluation framework experience
Translating ambiguous research ideas into working implementations
Running ablations, analyzing results, iterating quickly
Collaboration between engineers and research scientists

Common question themes

Walk me through debugging a broken or diverging training run — what did you check first

How would you scale a training pipeline across multiple GPUs/nodes, and what tradeoffs matter most

Tell me about an ablation study you ran — how did you isolate the variable that mattered

Describe implementing a research idea from a scientist that was underspecified or changed mid-experiment

What's your experience with RLHF or finetuning pipelines specifically

How do you decide how much engineering polish an experiment needs before running it

How candidates describe it

Real Research Engineer interview stories — retold from candidates' public write-ups, with sources.

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