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.
Step into this interview
Free · a live voice mock calibrated to this exact role
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
Related interviews
Cohere
Mid
Forward Deployed Engineer, Agentic Platform
Cohere
Mid
Member of Technical Staff, Applied ML
Cohere
Senior
Data Engineer, Data Foundations
Twilio
Mid
Machine Learning Engineer
Figma
Senior
Software Engineer, AI Product
Netflix
Senior