Principal Machine Learning Researcher (Physical AI)

Job Description

Job Description

PRINCIPAL MACHINE LEARNING RESEARCHER (PHYSICAL AI)

Freeform builds AI-native manufacturing systems that tightly integrate software, hardware, and physics to produce real-world parts at industrial scale. Our platform applies Physical AI to control and optimize complex manufacturing processes in real time—unlocking a new way to design, build, and scale hardware.

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 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.

Responsibilities :

• 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.

Basic Qualifications :

Nice to Have :

Location :

What We Offer :

Significant stock option packages

As a growing company, the salary range is intentionally wide as we determine the most appropriate package for each individual taking into consideration years of experience, educational background, and unique skills and abilities as demonstrated throughout the interview process. Our intent is to offer a salary that is commensurate for the company's current stage of development and allows the employee to grow and develop within a role.