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Software Engineer, GPU Infrastructure

Cohere · Seattle
✨ Reading the job post and pulling out the key points…
Full job post · as written by Cohere

WHO ARE WE?

Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.

We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.

We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!

WHY THIS TEAM?

Internal Infrastructure builds the infrastructure and tools used to train, evaluate, and serve Cohere's foundation models. You will work with AI researchers and ML engineers on stability, scalability, and observability. The work accelerates the models behind Cohere's platform, North.

You will run the shared Kubernetes fleets those workloads land on: multi-cloud GPU and TPU clusters, node health, performance, and the partnership with cloud providers and hardware vendors. The role includes a compensated 24x7 on-call rotation.

WHAT YOU WILL DO

  • 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.

We hire at more than one level. We set level based on the scope you've owned and how independently you drive work.

YOU MAY BE A GOOD FIT IF YOU HAVE

  • Run GPU Kubernetes clusters with thousands of GPUs for ML or HPC workloads.
  • Debugged Linux, networking, and performance problems. Familiarity with RDMA, NCCL, or accelerator health is a plus.
  • Written software in Python or Go. You would rather extend a solid open-source project than rebuild it.
  • Worked with researchers, ML engineers, or another team whose goals were not identical to yours.
  • Found a bottleneck, proposed a fix, and driven it in a fast-moving environment.

If this does not line up exactly with your experience, we still encourage you to apply.

FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS

  • A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
  • Full health and dental benefits, including a separate budget for mental health.
  • RRSP matching, 401K, Pension Scheme.
  • 100% Parental Leave top-up for up to 6 months, for either parent.
  • Annual enrichment benefits:

Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.

Education & learning stipend for conferences, courses, and coaching.

  • 6 weeks of paid vacation (30 working days!)
  • Budget for traveling to other offices if you are remote, plus an annual company offsite.

HOW AND WHERE WE WORK

  • Cohere is remote-friendly, but we also have…
Apply on Cohere's site ↗Opens Cohere's official careers page. Your application goes straight to them.
Company
Cohere
Location
Seattle
Work mode
Remote
Salary
$175K - $325K
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Apply on Cohere's site ↗Opens Cohere's official careers page. Your application goes straight to them.

Overview

You will manage GPU and TPU Kubernetes clusters for the internal infrastructure team, ensuring stability and performance for large-scale model training.

Skills

KubernetesPythonGoGPUTPULinuxNetworkingML Infrastructure

Responsibilities

  • Operate GPU Kubernetes clusters across multiple clouds
  • Partner with hardware vendors on reliability and performance
  • Automate GPU and fabric fault detection and remediation
  • Improve fleet utilization and cost tracking
  • Troubleshoot production failures and performance bottlenecks
  • Manage 24x7 on-call rotation

Requirements

  • Experience running large GPU Kubernetes clusters for ML/HPC
  • Proficiency in Python or Go
  • Strong Linux and networking debugging skills
  • History of working with ML engineers or researchers
  • Experience identifying and resolving technical bottlenecks
Nice to have
  • RDMA familiarity
  • NCCL experience
  • Accelerator health monitoring

About Cohere

Cohere builds foundation AI models and products for enterprise use cases.

What to prepare

Kubernetes cluster managementLinux system troubleshootingDistributed systems performanceML infrastructure scaling
Summarised by AI from the job post. Check the full post before applying.