Note: The job is a remote job and is open to candidates in USA. Runflow is hiring a DevOps Engineer to build and operate the infrastructure powering its AI image and video workflows. The role focuses on scaling a multi-cloud GPU fleet, Kubernetes workloads, queues and workers, orchestration services, databases, CI/CD, and observability.
Responsibilities
- 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
Skills
- 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
- 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)
- A k8s setup you built or rescued, repo, write-up, anything we can read
- GPU-fleet work, autoscaling logic, cost / latency tuning, even an internal post-mortem
- A Postgres query you tuned that mattered, before / after numbers
- A CI/CD pipeline you rebuilt that made the team faster
- Show us 1–3 things you've built that prove you can ship end-to-end
- What's a problem you solved with AI as the execution layer, not just chat?
- Show us a product you find beautiful. Why?
- Which AI tools are in your daily workflow?
- How much is AI helping you these days? A sentence or two
- Most recent role + company Just the title + company name
- Show off the production system you've built that you're most proud of
- And the gnarliest production issue you've personally debugged
- Share a Claude Code session that shipped a feature end-to-end
- When have you most successfully hacked a non-computer system to your advantage?
- Anything else? A short note, a question, a relevant detail we missed
Benefits
- Remote • global
- Senior IC + equity
- Paid challenge, 2 days — we pay for your time
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