Engineering Manager, GPU Infrastructure

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