GPU Infrastructure Engineer — Remote & Multi-Cloud Clusters

Jaide Health; California; Remote; USD 180,000 - 230,000; Full time

Benefits offered by this job

A weekly lunch stipend of $75/£75 or:"

Full health and dental benefits

RRSP matching, 401K, Pension Scheme

100% Parental Leave top-up for up to 6

Annual enrichment benefits: Arts &

Education & learning stipend

6 weeks of paid vacation (30 working)

Travel budget to other offices for rem

$500 home office stipend

Job summary

Cohere is seeking a seasoned Internal Infrastructure engineer to scale GPU Kubernetes clusters across clouds, partnering with researchers and ML engineers to improve stability, performance, and observability of our foundation models.

You will manage multi-cloud GPU/TPU workloads, automate fault detection, and drive cost efficiency while collaborating with cloud providers and hardware vendors. This role is remote-friendly and spans global teams.

Qualifications

• Experience deploying GPU Kubernetes clusters at scale.
• Strong Linux troubleshooting and networking skills.
• Experience working with ML researchers or engineers.

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

Python

Go

Linux

Networking

RDMA

NCCL

Performance tuning

SRE

Tools

Kubernetes

RDMA

NCCL

Job description

Cohere is seeking a seasoned Internal Infrastructure engineer to scale GPU Kubernetes clusters across clouds, partnering with researchers and ML engineers to improve stability, performance, and observability of our foundation models.

You will manage multi-cloud GPU/TPU workloads, automate fault detection, and drive cost efficiency while collaborating with cloud providers and hardware vendors. This role is remote-friendly and spans global teams.