PassiveLogic
Physical AI Engineer (Control, Optimization, Validation)
Holladay, US
Sponsorship not specifiedDetected 57 days ago
PythonC++SwiftAlgorithmsMachine LearningDeep LearningRoboticsMechanical DesignControlsCommunicationCollaborationThermodynamics
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odds of building a lasting career here
37Risky
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- This role combines control theory, optimization, machine learning, physics-based simulation, and systems validation.
Responsibilities
- Develop Autonomous Control and Optimization Design and implement autonomous control strategies that use physics-based digital twins for prediction, optimization, and decision-making.
- Develop scalable optimization algorithms using methods such as stochastic gradient descent, coordinate descent, distributed optimization, Bayesian methods, and evolutionary algorithms.
- Design and implement physics-based automated fault-detection and diagnosis algorithms.
- Develop fault-tolerant control methods that support degraded operation and system recovery.
- Develop autonomous agents, state-estimation methods, model adaptation, and control-correction algorithms.
- Validate Physical AI Systems Define performance metrics, acceptance criteria, and automated tests for autonomous capabilities and levels of autonomy.
- Verify fault-detection accuracy and validate fault-tolerant control, degraded operation, and recovery behavior.
- Compare simulation results with analytical solutions, laboratory measurements, and real-building data.
- We've reinvented the fundamental principles of automation to democratize technology, optimize buildings, and reduce the world's carbon footprint.
- Your work will span digital twins, autonomous reasoning, building physics, equipment behavior, and human comfort.
Requirements
- You Must Have MS or PhD in control engineering, computer science, robotics, applied mathematics, mechanical engineering, or a related field.
- You Should Have Experience with automatic differentiation and differentiable programming.
- Experience with software design, design patterns, and software architecture.
- It's Helpful to Have Experience with software-in-the-loop and hardware-in-the-loop testing.
- Experience with formal methods, probabilistic modeling, and graph neural network.
- Experience with building energy-modeling tools such as Dymola and EnergyPlus.
Benefits
- Integrate control and learning algorithms into production systems while meeting reliability and real-time performance requirements.
- Develop AI and Learning Systems Develop physics-informed predictive and learning models using deep learning, reinforcement learning, and transfer learning.
- Create learning methods that remain reliable under noise, outliers, sparse data, model uncertainty, and changing system behavior.
Company info
- We are a team of technologists, engineers, and creatives dedicated to making a sustainable impact through real-world solutions.
- We are looking for team members who have a passion for technology and want to work on cutting-edge problems with real-world solutions.
- Our culture is built on bringing together the most talented engineers, thinkers, and creatives - backed by the world's leading investors - working together to make the future a reality.
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This listing is sourced directly from PassiveLogic's careers page and normalized into a canonical job model.