ML Infrastructure Engineer
About The Position
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.
Requirements
- Experience building ML or data infrastructure at scale.
- Understanding of real-world constraints.
Responsibilities
- Building systems that make applied AI viable outside the lab.
- Working across high-throughput video pipelines handling millions of hours of data.
- Developing training and inference systems for multimodal / LLM-based models.
- Managing GPU infrastructure and performance optimization.
- Handling hybrid environments spanning edge, on-prem, and cloud.
Benefits
- Strong equity
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
