Most AI roles build on top of models. This one builds what makes them actually work.

We’re hiring ML Infrastructure Engineers to tackle a hard, real-world problem, understanding what’s happening on live job sites using wearable devices, large-scale video, and AI.

This isn’t clean benchmark data.

It’s messy, continuous, real-world input flowing from device → edge → cloud, at scale.

You’ll be working across:

• High-throughput video pipelines handling millions of hours of data

• Training and inference systems for multimodal / LLM-based models

• GPU infrastructure and performance optimisation

• Hybrid environments spanning edge, on-prem, and cloud

The role is end-to-end. Ingestion through to deployment.

You’ll be building the systems that make applied AI viable outside the lab.

The team comes from top AI and infrastructure companies, with strong funding and a clear technical roadmap. This is a systems challenge as much as an ML one.

 

San Francisco (on-site). $250k–$350k base + strong equity.

If you’ve built ML or data infrastructure at scale and care about real-world constraints, this is worth a conversation.

All applicants will receive a response.