Member of the Technical Staff - Platform

1 мес. назад

USALeadRemote

kubernetespostgres

Engineer responsible for building and operating the platform control plane managing large AI compute clusters.

We are looking for engineers to build and operate the control plane that runs our fleet. Your responsibilities will include:
The automated systems that take a cluster from bare machines to customer-ready
Machine lifecycle between tenants: join, wipe, verify, rejoin
Operating Kubernetes and Postgres across the fleet
Contributing to our custom Kubernetes operators
Scaling clusters from tens of nodes to thousands
Participating in on-call rotations

Impressive technical work you can go deep on, with impact in the world. That can take three years or twenty.
2+ years of on-call experience for critical production services
Deep Kubernetes experience
Strong Linux fundamentals: kernel, cgroups, containers, networking, storage
Experience operating databases, monitoring, CI/CD, and cloud infrastructure at scale
Familiarity with fleet management and capacity planning
Prior experience writing operators, or with GPUs, HPC scheduling, or bare-metal hardware is nice to have, but not required.
Cluster is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Location: North America Remote / San Francisco, CA · Full-Time
Compute is the most sought-after resource in the world, yet it still trades like commercial real estate: year-long contracts, manual fulfillment, capacity sitting idle because nobody can move it. We are building the liquidity layer at .
was founded by Nat Friedman and Daniel Gross to give startups the scaled AI infrastructure once reserved for hyperscalers. The first cluster filled almost instantly. The years since went into the platform that makes compute liquid: it deploys into foreign datacenters and turns the hardware it finds into clusters that leading AI labs train on.
Today operates compute for 80+ customers across 30+ capacity providers, with tens of thousands of GPUs under management and billions of GPU-hours supported, on everything from A100 to GB300.