Freeform is a 3D printing company offering metal 3D printing solutions for manufacturing companies. Freeform deploys software-defined, self-sufficient metal 3D printing factories around the world, bringing software scalability to physical production. Backed by 8VC.
About the role
Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts at the speed of human ideation. By treating manufacturing as a single integrated system, we unlock a new era of innovation where complex hardware is designed, built, and scaled without limits.
What they're looking for
- 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
More about this role
Freeform builds AI-native manufacturing systems that unify software, hardware, and physics to produce industrial-scale parts at the speed of human ideation. By treating manufacturing as a single integrated system, we unlock a new era of innovation where complex hardware is designed, built, and scaled without limits.
This architecture enables continuous generation of 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.
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...
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