Cohere
Staff
Build and operate the Kubernetes-based GPU superclusters that train Cohere's frontier models
Cohere's internal infrastructure team needs an engineer (the JD itself frames this 'As a Staff Software Engineer') to build and scale ML-optimized HPC infrastructure — Kubernetes-based GPU/TPU superclusters across multiple clouds — working directly with AI researchers on RDMA/NCCL-tuned distributed training. The interview covers deep Kubernetes-at-scale operations, low-level systems/networking knowledge, and self-service tooling design for researcher-facing workflows, plus 24x7 on-call ownership.
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Free · a live voice mock calibrated to this exact role
What this interview tests
- Kubernetes-based GPU/TPU supercluster operation at scale
- RDMA/NCCL/high-speed interconnect troubleshooting for distributed training
- Self-service tooling design for AI researchers
- Go (systems) and Python (ML tooling) proficiency
- Multi-cloud infrastructure cost/reliability/performance tradeoffs
- 24x7 on-call ownership of ML training infrastructure
Common question themes
How would you diagnose a distributed training job slowdown — network vs. compute vs. scheduler
Design a self-service tool that lets researchers debug their own training jobs
What breaks differently in Kubernetes when scheduling GPU/TPU ML workloads vs. stateless services
Tell me about a low-level Linux or networking issue you tracked down in a production ML cluster
How do you evaluate cost, reliability, and performance tradeoffs across multiple cloud GPU providers
Describe an on-call incident involving training infrastructure and how you resolved it
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