Remote-friendly GPU Infrastructure Engineer for Enterprise AI

Cohere; United States; Hybrid; USD 130,000 - 170,000; Full time

Benefits offered by this job

Lunch stipend

Health and dental benefits

RRSP/401K/Pension

Parental Leave top-up

Professional enrichment benefits

6 weeks of paid vacation

Home office stipend

Job summary

Cohere is seeking a skilled infrastructure engineer to operate GPU Kubernetes clusters across clouds and support frontier AI workloads. You will partner with researchers and ML engineers to ensure stability, scalability, and observability of our shared infrastructure.

Remote-friendly team, with offices in multiple global cities; you will help reduce downtime, optimize costs, and automate fault remediation while advancing performance for enterprise AI deployments.

Qualifications

• Experience running GPU Kubernetes clusters across clouds.
• Debug Linux, networking, and performance issues; familiarity with RDMA, NCCL or accelerator health is a plus.
• Proficiency in Python or Go; prefer extending open-source projects.
• Experience collaborating with researchers and ML engineers.
• Ability to identify bottlenecks and drive fixes in a fast-moving environment.

Responsibilities

• Deploy and operate GPU Kubernetes clusters across clouds, and bring up new hardware generations and providers.
• Work with cloud providers and hardware partners on reliability, performance, and cost, including RDMA and high-speed interconnects.
• Automate node, GPU, and fabric fault detection and remediation so most faults are fixed without a human.
• Improve fleet utilization, track cost, and cut the time it takes to get a faulty node back into service.
• Troubleshoot production failures and performance problems, and leave the system easier to operate than you found it.

Skills

GPU Kubernetes

Linux debugging

Networking

Python

Go

Open-source collaboration

Tools

Kubernetes

RDMA

NCCL

Job description

Cohere is seeking a skilled infrastructure engineer to operate GPU Kubernetes clusters across clouds and support frontier AI workloads. You will partner with researchers and ML engineers to ensure stability, scalability, and observability of our shared infrastructure.

Remote-friendly team, with offices in multiple global cities; you will help reduce downtime, optimize costs, and automate fault remediation while advancing performance for enterprise AI deployments.