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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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

How candidates describe it

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

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Google L4 Software Engineer Interview: Eight Rounds, No Offer

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Google L5 software engineer interview: phone screening, three onsite rounds, system design, and a late rejection

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Interviewed January 2023 · Not specified

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