Principal Machine Learning Researcher, Freeform

• 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

Desirable

• Freeform is hiring a Principal Machine Learning Researcher to lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system
• The 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
• Work includes modeling relationships between process inputs, geometry, and machine state to predict thermal, mechanical, and geometric outcomes during printing, using hybrid physics–ML approaches and multi-modal in-situ data
• Research is validated against physical outcomes and deployed into production systems, where improvements directly impact stability, yield, throughput, and capability across an expanding fleet of manufacturing nodes
• Your work will have a direct and meaningful impact on how frontier technologies are designed and produced at scale
• 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