Engineering Manager, GPU Infrastructure

Reposted 22 Days Ago
Be an Early Applicant
2 Locations
Remote
330K-400K Annually
Senior level
Artificial Intelligence • Machine Learning • Natural Language Processing • Software • Generative AI
The Role
Lead and mentor a GPU infrastructure engineering team to design, deploy, and scale GPU/TPU clusters. Define technical roadmap, implement topology-aware scheduling, monitoring, IaC, and cost/capacity optimization. Collaborate with AI researchers, cloud providers, and cross-functional teams to ensure reliable, secure, and performant infrastructure.
Summary Generated by Built In

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?

The GPU Clusters team builds and operates the superclusters that train Cohere’s frontier models. We sit at the intersection of hardware, distributed systems, and AI research. We work with cloud providers, researchers, and other infrastructure teams on problems few companies get to take on.

As an Engineering Manager, you’ll lead a team of engineers who care deeply about GPU infrastructure. You’ll set technical direction, grow people, and help the company scale a rapidly growing compute footprint.

 

As an Engineering Manager, you will:

  • Hire, mentor, and grow a team of GPU infrastructure engineers, including performance, career development, and technical guidance on hard infrastructure problems

  • Own the technical roadmap for the fleet: how we deploy, operate, and scale Kubernetes clusters, including workload scheduling, hardware fault detection, and performance

  • Partner with researchers and ML engineers so the training and inference stack works well on new GPU architectures

  • Work with cross-functional stakeholders such as Capacity, Finance, Legal, Security, and other infrastructure teams on planning, cost, compliance, and shared dependencies

  • Drive operational excellence: observability for GPU utilization and reliability, automation of cluster provisioning, cost optimization, and vendor relationships

You may be a good fit if you have:

  • Experience managing engineering or SRE teams, with a focus on technical mentorship, hiring, and growth, including in remote, distributed settings

  • A background running large Kubernetes compute fleets in production, including in multi-cloud environments: multi-cluster operations, scheduling, node health at scale, and familiarity with IaC and infrastructure monitoring

  • You’ve gone deep in one of the layers that make a GPU training fleet work, whether that’s cluster-wide operations, GPU networking, or hardware, and you’re willing to get hands-on and learn the rest

  • Experience with cost optimization and capacity planning for GPU infrastructure

  • A track record of partnering with researchers or ML engineers, and of making data-informed tradeoffs across reliability, cost, and delivery

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 offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.

  • For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.

  • For those not near an office: a co-working benefit so you can work alongside others in your city.

  • Everyone receives a $500 home office stipend to set up your workspace properly.

If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.


We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.

We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.

Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers page.

Skills Required

  • Experience managing engineering teams with technical mentorship and growth
  • Deep expertise in ML/HPC infrastructure (GPU/TPU clusters, distributed training)
  • Experience with distributed training frameworks: JAX, PyTorch, TensorFlow
  • Proven experience with Kubernetes at scale for AI workloads in multi-cloud environments
  • Familiarity with observability and monitoring tools (Prometheus, Grafana)
  • Experience with Infrastructure-as-Code tools (Terraform, ArgoCD, or similar)
  • Experience with cost optimization and capacity planning for GPU infrastructure
  • Track record collaborating with AI researchers or ML engineers
  • Strong communication skills and experience working in remote, distributed teams
  • Ability to manage vendor relationships and negotiate hardware/cloud contracts
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The Company
HQ: Toronto, Ontario
224 Employees
Year Founded: 2019

What We Do

Cohere provides unprecedented access to affordable, easy-to-deploy large language models. Our platform gives computers the ability to read and write - whether you want to better understand what your customers are saying, or you want to write compelling copy that speaks to your target audience, Cohere can help.

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