Cohere
Mid
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.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- 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
常见提问方向
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
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