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Salary: USD 200,000 - 500,000 / annual
Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts at the speed of human ideation. The company treats manufacturing as a single integrated system, generating petabyte-scale, high-fidelity data capturing the physics of metal printing—from in-situ process signals and machine state to geometry and material outcomes. Each factory node contributes to a growing learning system that improves modeling accuracy, control performance, yield, and scalability over time.
You will 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.
Key responsibilities include:
- 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
- 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 linking process parameters, geometry, and machine state to thermal and mechanical outcomes
- Integrate learned models with physics-based simulation and digital twin frameworks
- Contribute to design of closed-loop control and autonomy systems operating in real time on production hardware
- Develop learning-based approaches for machine health monitoring, anomaly detection, and system diagnostics
- Guide 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
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 direct and meaningful impact on how frontier technologies are designed and produced at scale.
The role is based in Hawthorne at Freeform's vertically integrated facility, which brings technology development, R&D, and production together under one roof. The company operates at the center of LA's deep tech ecosystem. This role requires full-time onsite presence (five days a week), with very limited exceptions.
**Requirements:**
- PhD in computer science, applied mathematics, physics, robotics, controls, or 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
**Nice to Have:**
- 10+ years of industry experience
- Experience with hybrid physics–ML models, digital twins, or simulation-in-the-loop learning
- Background in autonomy, robotics, model predictive control, optimal 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
