Principal Machine Learning Researcher (Physical AI)
Core
Lead the development of advanced learning and control problems in a production-scale, AI-native metal manufacturing system, integrating large-scale physical data with physics-based simulation for closed-loop control and autonomy.
Role type
Principal Machine Learning Researcher (Physical AI)
Builds
AI-native manufacturing systems for industrial-scale metal parts
Domain
Manufacturing / Physical AI / Metal Additive Manufacturing
Deliverable
production ML models
Required skills
Machine learning for physical systems, hybrid physics-ML modeling, Python, C/C++, large-scale noisy real-world datasets, unsupervised/self-supervised learning, digital twin frameworks, model predictive control, anomaly detection
Preferred skills
Applied mathematics, robotics, autonomy, image-based inference, computational geometry
Technologies
Python, C/C++, digital twins, physics-based simulation
Responsibilities
Design and develop machine learning models for complex, multi-physics manufacturing processes; Develop hybrid modeling approaches combining first-principles physics with data-driven learning; Lead formulation of learning-based models for prediction and control in production-scale metal additive manufacturing systems; Develop methods to learn from large-scale, high-dimensional in-situ sensor data; Design unsupervised and self-supervised learning techniques to correlate process signals with part quality; Develop models linking process parameters 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; Develop learning-based approaches for machine health monitoring and diagnostics; Guide integration of ML models into production workflows; Define research direction and technical standards for ML applied to physical systems.