Description review
VLM Run ( ) | 1x Founding Infrastructure Engineer | In-Person (Bay Area) | Full-time
VLM Run ( ) · In-Person (Bay Area) · back to the listing
HR standards
36/100
poor
Title ↔ description
45/100
poor
Reads as
Machine Learning Engineer
95% confident
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
We’re building the inference platform for visual intelligence. We’re a deeply technical team of AI / computer-vision engineers (
20+ years1
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 [email protected] 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).
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 [email protected] 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).