GPU Infrastructure Engineer – Kubernetes & Multi-Cloud

Cohere; Seattle (WA); Hybrid; USD 180,000 - 260,000; Full time

Benefits offered by this job

Weekly lunch stipend: $75/£75 or local

Health and dental benefits

RRSP matching / Pension

Parental leave top-up

Work enrichment stipend

Conferences & learning stipend

6 weeks paid vacation

Travel budget between offices and off‑

Home office stipend

Job summary

Cohere is seeking an experienced infrastructure engineer to run GPU Kubernetes clusters and optimize stability, performance, and cost across multi-cloud environments. You will work with AI researchers and ML engineers to ensure high reliability of Cohere's foundation models and platform.

You will automate fault detection, improve fleet utilization, and collaborate with hardware partners. Remote-friendly, with on-site options in major offices, you’ll join a team focused on scalable, secure AI

Qualifications

• Run GPU Kubernetes clusters with thousands of GPUs for ML or HPC workloads.
• Debug Linux networking and performance issues; familiarity with RDMA/NCCL is a plus.
• Write software in Python or Go and extend open-source projects.
• Collaborate with researchers, ML engineers, or other teams to align goals.
• Identify bottlenecks and drive fixes in a fast-moving environment.

Responsibilities

• Deploy and operate GPU Kubernetes clusters across clouds.
• Collaborate with cloud providers and hardware partners on reliability and cost.
• Automate fault detection and remediation for nodes and GPUs.
• Improve fleet utilization and reduce time to repair faulty nodes.
• Troubleshoot production issues and improve overall operability.

Skills

GPU Kubernetes

Linux networking

Python

Go

RDMA/NCCL

Tools

Kubernetes

RDMA

NCCL

Job description

Cohere is seeking an experienced infrastructure engineer to run GPU Kubernetes clusters and optimize stability, performance, and cost across multi-cloud environments. You will work with AI researchers and ML engineers to ensure high reliability of Cohere's foundation models and platform.

You will automate fault detection, improve fleet utilization, and collaborate with hardware partners. Remote-friendly, with on-site options in major offices, you’ll join a team focused on scalable, secure AI