Principal ML Researcher: Physical AI & Manufacturing Systems

Freeform; Los Angeles (CA); Sur place; USD 150 000 - 230 000; Plein temps

Résumé du poste

Freeform is seeking a Principal Machine Learning Researcher to lead the development of advanced learning and control in a production-scale, AI-native metal manufacturing system. The role focuses on integrating large-scale physical data with physics-based simulation and embedding these models into closed-loop control and autonomy frameworks.

Responsibilities include designing multi-physics ML models, learning from in-situ sensor data, and linking process parameters to thermal/mechanical outcomes,

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.

Responsabilités

• 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.

Connaissances

Python programming

C/C++

Large-scale datasets

Hybrid physics–ML

Digital twin

Autonomy/Control

Reinforcement learning

Formation

PhD in CS/Math/Physics/Robotics/Controls

Description du poste

Freeform is seeking a Principal Machine Learning Researcher to lead the development of advanced learning and control in a production-scale, AI-native metal manufacturing system. The role focuses on integrating large-scale physical data with physics-based simulation and embedding these models into closed-loop control and autonomy frameworks.

Responsibilities include designing multi-physics ML models, learning from in-situ sensor data, and linking process parameters to thermal/mechanical outcomes,