Kubernetes Cluster Engineer - HPC/AI Platforms

Overview

We are seeking a skilled Kubernetes Cluster Engineer to support the design, deployment, and ongoing operations of large-scale, GPU-dense compute environments where Kubernetes and Slurm run side by side. This role centers on cloud-native platform engineering — cluster lifecycle, GitOps-driven configuration, operators, and multi-tenant access control — applied to current-generation, GPU-dense bare metal rather than typical microservice workloads. You will own the Kubernetes layer of clusters that schedule AI/ML training and inference jobs, working closely with researchers, vendors, and partners. Slurm expertise is not a prerequisite, but you should be comfortable working next to it and interested in learning it: our environments increasingly bridge the two schedulers through Slinky, and that seam is where much of the interesting work lives.

You will work alongside our team to support in-house, partner, and customer infrastructure.

Why This Role

You will personally operate the newest silicon.

NVIDIA B300 and AMD MI-series clusters, on bare metal.

Breadth instead of a single monolith.

Multiple clients, multiple GPU vendors and generations, and greenfield bring-ups where you make the architectural calls rather than inherit them. You will not be the third Kubernetes engineer maintaining someone else's cluster.

A small team and real autonomy.

Team oriented project management. You will own systems end to end, talk directly to the researchers and vendors who depend on them, and see the consequences of your choices quickly.

A path into HPC depth.

Kubernetes fluency is common; Kubernetes fluency plus real Slurm and HPC scheduling expertise is scarce and getting scarcer as AI infrastructure converges on both. We will actively work with you to develop that second half, because we need it and because it makes you harder to replace anywhere you go next.

Responsibilities

Kubernetes Platform Engineering

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