Head of MLE

Ironsite builds AI technology for construction projects using wearable hardware and construction footage. The Head of ML Engineering owns the systems for model experimentation, labeling, research data tooling, ML-backed product features, and production orchestration and inference, while initially serving as a hands-on technical lead.

Responsibilities:

• Build what research needs to A/B test models in production: run two models side by side on real sites, compare their output, and decide what ships. When a test wins, roll it out everywhere
• Own Tagging Interfaces, the tool our labeling team uses every night. Pre-label footage with models so labelers correct rather than start from scratch, decide what gets labeled next based on what research needs, and track throughput and label quality
• Make 100,000+ hours of footage easy to use: dataset versioning, search across footage and labels, and fast assembly of training sets
• Ship ML-backed features in the superintendent report. When a customer gets a bad report, trace it to the model, the labels, or the pipeline and fix it. Get models working on each new jobsite during onboarding
• Own the orchestrator that runs the nightly pipeline, plus model deployment, versioning, rollback, and inference. Watch output quality by site and catch drops before customers do. Own GPU and cloud spend, and absorb footage and device changes from the hardware team without breaking the pipeline
• Research wants answers in days, not weeks. Customers need every 5 AM report to be right. You'll build a system that gives research both
• Every model improvement starts with labeled footage. You'll get more useful labels out of every labeler hour, month over month
• Each new jobsite adds footage, but the report is still due at 5 AM. You'll scale the pipeline without cost growing at the same rate
• You'll write much of the code yourself while hiring the people who take it over