DevOps Engineer.

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We're hiring

• Infrastructure

DevOps Engineer.

Build the infrastructure that powers the next generation of AI media. GPU fleet, warm workers, queues, FastAPI services, in production tomorrow.

Remote

• global

Full-time / contract

Senior IC + equity

Apply now How we hire

The role

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. Workers, GPU pool, queues, the database, the FastAPI services that orchestrate everything, all of it is moving fast and could move faster with the right person.

If you've run Kubernetes in anger, sized container fleets to demand without over-paying for idle GPUs, debugged CrashLoopBackOff at 3am, and have strong opinions on when to use a queue vs. a stream, this is for you.

Quick facts

TeamInfrastructure

Reports toCTO

LocationRemote

• global

TypeFull-time or contract

CompensationSenior IC + equity

StartASAP

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.

Project 01

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.

Project 02

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.

Project 03

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...

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