Staff Software Engineer, ML Infrastructure

Who We Are Voxel is building the future of Computer Vision and Machine Learning for operations, risk, and safety. We use computer vision and AI to enable existing security cameras to automatically detect hazards and high-risk activities, keep people safe and drive operational efficiencies. Our technology addresses the key cost drivers for workers’ compensation, general liability, and property damage, which cost US employers over $500 billion annually. Our customers include Fortune 500 companies across grocery, retail, manufacturing, food and beverage, logistics, and pharmaceutical distribution. We’ve passed $10M ARR with strong expansion revenue. Based in SF, backed by industry-leading VCs. About the Role Voxel’s perception system is the technical core of everything we ship. Our models detect human activity, equipment interactions, environmental hazards, and operational state in real time across thousands of cameras in manufacturing, logistics, retail, and pharmaceutical environments. Safety was our wedge; it proved our platform works. Now customers are pulling us into operations: equipment utilization, workflow compliance, process efficiency. Every new use case runs through the perception team. We're hiring a Staff Software Engineer to own ML Infrastructure at Voxel. Our applied ML team is shipping vision models into production every week, across thousands of cameras at Fo [... source excerpt omitted ...] foundational systems that the applied ML team relies on, and shape the architectural decisions that will define our ML stack for the next several years. This is a hands-on role. You'll write code, make architecture calls, and own outcomes end to end. You'll partner closely with applied CV engineers, the ML Data team, and the Platform team, and you'll be the technical voice in the room when ML infrastructure tradeoffs come up. What You'll Do Set the technical direction for ML infrastructure at Voxel: what we build, what we buy, and how the pieces fit together as the team and model portfolio scale Architect and build the training infrastructure that lets the applied ML team [... source excerpt omitted ...] h, AWS) Own the train-to-deploy handoff: export trained models to optimized inference formats (TensorRT, ONNX), quantify accuracy and latency impact, and partner with Platform on production deployment Pick and roll out the experiment tracking and lifecycle stack (Weights & Biases, MLflow, ClearML, or similar) so researchers can run, compare, and reproduce experiments efficiently Establish DevOps-for-ML best practices (IaC, CI/CD, observability, cost monitoring) so researchers can iterate quickly and safely Mentor engineers across Vision & AI on ML infrastructure best practices, raising the bar for how the org thinks about training, evaluation, and deployment Anticipate where t