Founding Engineer — ML Platforms Engineer

About the role

Cumulus Labs builds the software that turns raw GPU capacity into fast, cheap, production AI. We're looking for an ML Platforms Engineer to help build and run the orchestration layer underneath our inference and agent products, the system that schedules workloads, allocates GPUs, and keeps a heterogeneous, multi-cloud fleet running at high utilization.

We care more about how you think than which languages are on your resume. Our stack today includes Go, Kubernetes, and Terraform, but we're looking for someone who can walk into any part of a production system, understand it, and make it better, not someone who only knows one toolchain.

What you'll do

• Build and extend our GPU orchestrator: scheduling, fractional allocation, live workload migration across GPUs with no downtime

• Design and evolve multi-tenant primitives: quotas, isolation, usage metering, a tenant-facing inference gateway

• Own observability for the fleet: metrics, logs, and traces at scale

• Debug hard, systems-level problems across the stack, from scheduling logic down to GPU memory and networking

• Make real architectural decisions, not just implement someone else's design

• Ship fast, own your systems end to end, and work directly with the founder

What we're looking for

• Excellent fundamentals: data structures, algorithms, distributed systems concepts, and the judgment to apply the right pattern to the right problem

• Real production experience, ideally with systems that had to stay up and scale under load

• Strong design instincts: you can reason about tradeoffs, not just follow a framework's conventions

• Fast learner who can go deep in unfamiliar territory; specific experience with Go or Kubernetes is a plus, not a requirement

• Comfortable using modern AI coding tools (we use Claude Code heavily) to move fast without losing rigor

• You want to work in person, in a small team, solving problems nobody has solved before

Why Cumulus

We're small, early, and building the systems layer for the next generation of AI infrastructure. You'll own real infrastructure from day one, not tickets in a backlog.

Interview Process

1. Intro call with a founder (30 min) — background, mutual fit

2. Technical deep dive (30-45 min) — a real problem from our orchestrator, discussed live

3. Take-home or paired session on a scoped systems problem

4. References

We move fast: most candidates hear back within a few days at each stage, and we aim to get from first contact to offer in under two weeks.