Principal Machine Learning Researcher (Physical AI)

Lead development of machine learning methods for complex, multi-physics manufacturing processes within a production-scale metal additive manufacturing system. Develop hybrid physics–ML models, integrate learning-based predictors with physics-based simulation and digital twin frameworks, and contribute to closed-loop control and autonomy on production hardware. Work with large-scale in-situ sensor data to relate process inputs, geometry, and machine state to thermal, mechanical, and geometric outcomes using unsupervised/self-supervised learning techniques. Collaborate across theory, experimentation, and deployment, guiding research direction, standards, and interfaces for ML in physical systems. Requires 5+ years of experience in ML/applied research or a PhD in related fields, strong Python skills (and C/C++), and experience with real-world datasets. Location is El Segundo, California with full-time onsite requirement; relocation assistance provided.