ML Infra Engineer — Scale Real‑World AI (SF On-site)

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. #J-18808-Ljbffr Trades Workforce Solutions