Principal Engineer Physical AI Systems Modeling

Originally posted 6 November 2026 by the employer. Relisted by the employer on 11 November 2026.

About the role

As a Principal Engineer (Physical AI - System Modeling) in the Pathfinding Studio within the Engineering Innovation Office, you will define and lead research in foundation model training and fine-tuning, vision-language-action models, reinforcement learning, and generative models. You will lead multidisciplinary work spanning virtual system modeling, data curation, learning, perception, planning, control, and translation of research into hardware implementations.

What you'll do

• Define the long-term Physical AI research direction and system architecture for humanoid and industrial robotics.
• Lead foundation model training and fine-tuning, vision-language-action (VLA) model development, reinforcement learning, and generative modeling connecting perception, planning, control, and manipulation.
• Architect, develop, and maintain virtual system world models covering articulated hands and arms, sensors, robot dynamics, contact interactions, and environments.
• Own the technical architecture of Physical AI data curation pipelines across simulation, synthetic data generation, real-world teleoperation, multimodal synchronization, and dataset versioning.
• Lead imitation-learning, reinforcement-learning, domain-randomization, sim-to-real, and real-to-sim workflows.
• Translate research into hardware implementations through algorithm–hardware co-design and evaluate system-level performance and resource trade-offs.

What you'll need

• Ph.D. in Robotics, Engineering Science, Computer Science, Electrical Engineering, Computer Engineering, Mechanical Engineering, or a closely related field, plus 8 years of related research and engineering experience.
• Expertise in foundation model training and fine-tuning, vision-language-action models (VLAs), reinforcement learning, and generative and world models for humanoid and industrial robotics.
• Hands-on experience with NVIDIA Isaac Sim, MuJoCo, and similar physics-based simulation and virtual system modeling platforms.
• Deep expertise in robotics algorithms including kinematics, dynamics, perception, planning, control, grasping, manipulation, trajectory generation, and contact-rich interaction.
• Experience in digital ASIC design and verification, collaborating across research, system architecture, RTL design, and verification disciplines.
• Software development proficiency in Python and C/C++ in CPU- and GPU-based environments with version control, testing, and reproducible experimentation.
• Track record of publications in robotics conferences and journals.

Skills: Physical AI, foundation models, algorithm-hardware co-design, neuromorphic, ASIC

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