Founding Infrastructure Engineer
VLM Run
VLM Run builds an inference platform for visual intelligence and operates the VLM Run Gateway, a unified API serving open-weight VLMs, embodied VLAs and ViTs across GPUs and clouds. This role owns the inference infrastructure and scaling of the gateway to handle high request volumes.
What you’ll do
- Own the inference infrastructure for the VLM Run Gateway
- Scale the gateway to serve hundreds of thousands of requests per day
- Operate serving across a fleet of GPUs and multiple clouds
What they’re looking for
- Experience with Ray, k8s, GPUs, serverless (especially called out)
- Provide GitHub profile, papers, and ML projects recently shipped (100+ GitHub stars only)
- Be located in the Bay Area (no remote work)
Skills
- Ray: Ray, distributed
- Kubernetes: k8s, Kubernetes
- GPUs: GPUs, fleet of GPUs
- Serverless: serverless
- Vision models: VLMs, VLAs, ViTs, open-weight VLMs
- API / Gateway: VLM Run Gateway, unified API
The post
VLM Run (https://vlm.run) | 1x Founding Infrastructure Engineer | In-Person (Bay Area) | Full-time
We’re building the inference platform for visual intelligence. We’re a deeply technical team of AI / computer-vision engineers (20+ years combined, MIT/CMU/NC State PhDs) who’ve shipped production ML infra across autonomous driving and LLMs.
We launched the VLM Run Gateway (https://vlm.run/gateway) in September, a unified API that serves open-weight VLMs, embodied VLAs, ViTs, served across a fleet of GPUs and clouds. That's exactly the infrastructure problem this role will own. We're already scaling to serve 100s of thousands of requests per day, so if this sounds exciting to you, read on.
Email us at hiring@vlm.run with your GitHub profile, papers, ML projects you’ve recently shipped (100+ GH stars only) - especially with Ray, k8s, GPUs, serverless. No AI text please, keep it short, shorter emails are more likely to get a response. No remote work, must be in the Bay Area (specify in email subject).