Physical AI Engineer; Control, Optimization, Validation

Can you build intelligent systems that understand physical environments, make autonomous decisions, and demonstrate measurable real-world value? Passive Logic is looking for a Physical AI Engineer to develop, optimize, and validate our autonomous control system, AI models, and levels of autonomy.

Passive Logic is the first fully autonomous platform for buildings. We’ve reinvented the fundamental principles of automation to democratize technology, optimize buildings, and reduce the world’s carbon footprint. 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.

You will develop and validate the algorithms that enable buildings to understand their physical state, learn from experience, and autonomously optimize their operation.

This role combines control theory, optimization, machine learning, physics‑based simulation, and systems validation. Your work will span digital twins, autonomous reasoning, building physics, equipment behavior, and human comfort. You will develop production‑quality algorithms and test them in simulation, laboratory systems, and real buildings.

• Design and implement autonomous control strategies that use physics‑based digital twins for prediction, optimization, and decision‑making.
• Translate comfort, energy, equipment life, and operational requirements into control objectives, constraints, and cost functions.
• 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.
• Integrate control and learning algorithms into production systems while meeting reliability and real‑time performance requirements.

• Develop physics‑informed predictive and learning models using deep learning, reinforcement learning, and transfer learning.
• Develop autonomous agents, state‑estimation methods, model adaptation, and control‑correction algorithms.
• Create learning methods that remain reliable under noise, outliers, sparse data, model uncertainty, and changing system behavior.

• Define performance metrics, acceptance criteria, and automated tests for autonomous capabilities and levels of autonomy.
• Test system behavior under disturbances, incomplete observations, model divergence, and equipment, sensor, or communication failures.
• 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.

If your experience does not meet all our posted requirements below, we’d still love to hear from you. We are looking for practitioners who are passionate about understanding people, committed to lifelong learning, and driven by the love of what they do. If that’s you, please apply!

• MS or PhD in control engineering, computer science, robotics, applied mathematics, mechanical engineering, or a related field.
• Demonstrated expertise in AI development, scientific machine learning, optimal control, reinforcement…