Physical AI Engineer (Control, Optimization, Validation)
Core
Develop, optimize, and validate autonomous control systems and AI models for fully autonomous building platforms using physics-based digital twins and real-world data.
Role type
Senior IC Physical AI Engineer (Control, Optimization, Validation)
Builds
Autonomous control strategies, physics-informed predictive models, fault-detection algorithms, and production systems for building automation.
Domain
Building automation, HVAC systems, thermodynamics, energy modeling, and physical AI.
Deliverable
production ML models | product features
Required skills
optimal control, reinforcement learning, model predictive control, state estimation, system identification, physics-informed machine learning, stochastic gradient descent, distributed optimization, fault-tolerant control, automatic differentiation, differentiable programming, Python, C++, Swift
Preferred skills
software-in-the-loop testing, hardware-in-the-loop testing, formal methods, probabilistic modeling, graph neural networks, vector/SIMD/tensor computational methods, Dymola, EnergyPlus
Technologies
Python, C++, Swift, Dymola, EnergyPlus
Responsibilities
Design and implement autonomous control strategies using physics-based digital twins; develop scalable optimization algorithms and fault-detection methods; create physics-informed predictive and learning models; define performance metrics and validate autonomous capabilities in simulation and real buildings.
Seniority
Senior, hands-on IC
