Machine Learning Infrastructure Engineer

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 dataTraining and inference systems for multimodal / LLM-based modelsGPU infrastructure and performance optimisationHybrid environments spanning edge, on-prem, and cloudThe 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 equityIf 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.