> Markdown version of [/jobs/ext/3692119-principal-machine-learning-researcher-physical-ai](https://www.wearedevelopers.com/jobs/ext/3692119-principal-machine-learning-researcher-physical-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# PRINCIPAL MACHINE LEARNING RESEARCHER (PHYSICAL AI)

- **Company:** International Family Entertainment, Inc.
- **Location:** Torrance, CA, United States
- **Experience:** Expert
- **Salary:** $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Python (Programming Language), Machine Learning, Computational Geometry, Reinforcement Learning, Digital Twin, Information Technology, Data Analytics
- **Published:** October 11, 2026
- **Apply:** https://www.juju.com/job/16_7dbdefb01

## About the Role

* 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.

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.

## Description

* 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.