- Location
- San Francisco, CA
- Workplace
- Hybrid, Onsite
- Type
- Full-time
- Department
- Engineering
- Closing date
- Today
- Source
- Vincere
Description
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.
