Founding Engineer — ML Platforms Engineer

3 нед. назад

150k–300k USD / yearUSASeniorOnsite

gokubernetesterraformgpudistributed systemsmetricslogstracing

Build and maintain GPU orchestration and multi-tenant systems for AI production infrastructure.

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.
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

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

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.