Principal ML Researcher, Physical AI for Metal Printing

Freeform; Hawthorne (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Freeform seeks a Principal Machine Learning Researcher to lead development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system. You will design models that fuse large-scale physical data with physics-based simulation and embed them into closed-loop control and autonomy frameworks.

The role focuses on modeling relationships between process inputs, geometry, and machine state to predict thermal, mechanical, and geometric outcomes during printing,

Qualifications

• PhD in computer science, applied mathematics, physics, robotics, controls, or a closely related discipline.
• 5+ years of experience in machine learning, applied research, or related technical field.
• 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).
• Experience working with large-scale, noisy, real-world datasets.

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.
• Develop methods to learn from large-scale, high-dimensional in-situ sensor data collected during printing.
• Design unsupervised and self-supervised learning techniques to correlate process signals with part quality, geometry, and performance.
• Develop models that link process parameters, geometry, and machine state to thermal and mechanical outcomes.
• Integrate learned models with physics-based simulation and digital twin frameworks.
• Contribute to the design of closed-loop control and autonomy systems that operate in real time on production hardware.
• Develop learning-based approaches for machine health monitoring, anomaly detection, and system diagnostics.
• Guide the integration of machine learning models into production software and manufacturing workflows.
• Help define research direction and technical standards for machine learning applied to physical systems within the organization.

Job description

Freeform seeks a Principal Machine Learning Researcher to lead development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system. You will design models that fuse large-scale physical data with physics-based simulation and embed them into closed-loop control and autonomy frameworks.

The role focuses on modeling relationships between process inputs, geometry, and machine state to predict thermal, mechanical, and geometric outcomes during printing,