Senior Technologist, Machine Learning

Precision product development is one of the last frontiers where AI has not yet been systematically deployed at the physics level. We are changing that. As the ML Technical Lead on this team, you will be the person who makes physics-informed AI work in product development — not as a research prototype, but as a deployed system that drives real engineering decisions.

If that is the kind of problem you want to work on, we want to talk to you. Key

Deep Learning Systems Architecture & Ownership : Design and own CNN, U-Net, and ViT systems for precision inspection, measurement, and defect classification. Own real-time anomaly detection from sensor and time-series product development data streams. Highest day-to-day output responsibility.

Physics-Informed AI & Surrogate Modeling: Validate and deploy PINNs methodology originated by the ARS — own product development implementation, physics-constraint validation against engineering requirements, and surrogate model pipelines. You are the product development -side authority; the ARS designs the methodology; you validate and deploy it to real product development systems. Active Learning Pipeline Architecture: Architect active learning pipelines and Bayesian experimental design frameworks — integrating ARS-designed acquisition functions with DE-supplied feature data and laboratory scheduling systems.

This workstream directly reduces physical experiment cost. MLOps Platform Ownership: Own the full ML platform: M

Lflow, Doc