DevOps Engineer.

Runflow runs millions of AI image and video jobs a month across a multi-cloud GPU fleet. Every workflow our customers ship hits our infrastructure: containers spin up with the right models loaded, queues drain into warm workers on the right GPU class, jobs run to completion within a latency budget, and Sentinel scores them before they go out. We're hiring a DevOps Engineer to help build that infrastructure.

Representative projects

What you'd ship in your first 6 months. Concrete, shippable, on the live roadmap. Not hypothetical. We expect every one of these to land.

• Push the multi-cloud GPU fleet to the next level
  RunPod is the primary today, with fallback providers across A100 / H100 / 4090 classes. Tighten the auto-scaling against queue depth, kill the cold-start tax, route the job to the cheapest GPU class that meets the latency budget.
• Make warm-worker fleets boring to operate
  ComfyUI workers stay alive between runs with models in GPU memory. Tune the worker pool, reclaim idle GPUs without losing the warm-cache win, ship dashboards so on-call doesn't have to grep logs.
• Build the per-run cost analytics surface
  Per-node GPU time, per-run cost, queue wait, top cost drivers. Customers + ops both need this. The hard part isn't the dashboard, it's the accurate per-node attribution at fleet scale.

Responsibilities

What you'll be doing. You'll own the full lifecycle, from prototype to production at scale.

• Operate the multi-cloud GPU fleet (RunPod primary, fallback providers, A100 / H100 / 4090)
• Run + scale Kubernetes workloads, kill CrashLoopBackOff before it pages anyone
• Tune queue + worker architecture so jobs land on warm GPUs at the right tier
• Manage the CUDA / driver / model-cache layer so containers spin up fast
• Ship + maintain Python FastAPI services that orchestrate the fleet
• Own the production Postgres + Redis (Neon, schema migrations, connection pooling, hot paths)
• Maintain the CI/CD pipeline + merge queue (GitHub Actions today, room to rebuild)
• Build observability so you can answer 'what just changed' in 30 seconds, not 30 minutes

Requirements

About you. You may be a good fit if…

• You've operated Kubernetes in production, not just used managed clusters as a black box
• You've debugged a real GPU-fleet incident, autoscaling gone wrong, container OOM, driver mismatch, the works
• You're comfortable in Python (FastAPI services, scripts, glue) and shell, even if neither is your home language
• You think in queue depth, p95 latency, cost per request, not just CPU + memory graphs
• You can read a Postgres query plan and know when an index is the answer (and when it isn't)
• You have strong opinions on observability, what to log, what to trace, what to alert on, what to ignore

Strong candidates also have…

• Have run a multi-cloud GPU fleet (RunPod, Modal, AWS, Lambda Labs, etc.) at meaningful scale
• Have built or maintained CI/CD systems with merge queues, parallel test sharding, etc.
• Have rebuilt a system that was 'on-call hell' into one that runs itself
• Have shipped infrastructure-as-code (Pulumi, Terraform, k8s manifests) that survived 6+ months without rewriting
• Have contributed to OSS in the infra space (k8s controllers, GPU schedulers, runtime tools)

What we're not looking for

• A specific number of years
• A specific degree
• A specific stack

We hire on whether you can ship end-to-end and whether you have taste. Everything else is noise.

Tech stack

• Backend: Python, FastAPI, Node.js, PostgreSQL, Redis
• Infra: Kubernetes, Docker, RunPod, Modal, GitHub Actions
• GPU layer: CUDA, A100 / H100 / 4090 fleet, ComfyUI workers
• Observability: Grafana, OpenTelemetry, Loki, Sentry

Team: Infrastructure
Reports to: CTO
Location: Remote global
Type: Full-time or contract
Compensation: Senior IC + equity
Start: ASAP