Principal Machine Learning Researcher (Physical AI)

Develop machine learning models for AI-native manufacturing systems integrating physical data and physics-based simulation.

• •Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts.
• •This role focuses on developing machine learning methods that integrate large-scale physical data with physics-based simulation and embedding these models into closed-loop control and autonomy frameworks.
• •Key Responsibilities Design and develop machine learning models for complex, multi-physics manufacturing processes.
• •Develop hybrid modeling approaches that combine first-principles physics with data-driven learning.
• •Lead the formulation of learning-based models used for prediction and control in production-scale metal additive manufacturing systems.
• •Requirements Develop models that link process parameters, geometry, and machine state to predict thermal, mechanical, and geometric outcomes during printing.
• •Design unsupervised and self-supervised learning techniques to correlate process signals with part quality, geometry, and performance.
• •Research is validated against physical outcomes and deployed into production systems.