Principal Machine Learning Researcher (Physical AI)

Join Freeform as a Principal Machine Learning Researcher, where you will lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system. Your work will have a direct impact on how frontier technologies are designed and produced at scale. You will design and develop machine learning models for complex, multi-physics manufacturing processes, and contribute to the design of closed-loop control and autonomy systems. Enjoy significant stock options, 100% employer-paid medical, dental, and vision insurance, and a flexible work environment.

• Lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system.
• Design and develop machine learning models for complex, multi-physics manufacturing processes, integrating large-scale physical data with physics-based simulation.
• Guide the integration of machine learning models into production software and manufacturing workflows, ensuring improvements in stability, yield, throughput, and capability.
• Experience working with large-scale, noisy, real-world datasets
• Strong foundations in machine learning applied to physical systems, modeling, or control
• Proficiency in Python and at least one systems-level programming language (C/C++ preferred)
• 5+ years of experience in machine learning, applied research, or related technical fields or a PhD in machine learning, applied mathematics, physics, robotics, controls, or a closely related discipline
• MS or PhD in applied mathematics, physics, robotics, controls, materials science, or a related discipline
• Experience with hybrid physics–ML models, digital twins, or simulation-in-the-loop learning
• Background in autonomy, robotics, model predictive control, or reinforcement learning for physical systems
• Experience with image-based or sensor-based inference in industrial or scientific settings
• Familiarity with computational geometry or geometric modeling
• Comfort working across theory, experimentation, and deployment in tightly coupled systems
• Ability to reason from first principles and translate theory into working models and systems