Physical AI Engineer (Control, Optimization, Validation)

Aimlroles; Holladay (UT); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical, dental, and vision coverage

Disability and life insurance options

Flexible paid time off

Free catered lunch in the office

Free National Parks pass

Free ski pass

Job summary

PassiveLogic seeks a Physical AI Engineer to develop, validate, and optimize autonomous control systems for buildings. You’ll build production-grade AI models, digital twins, and autonomy levels to understand physical states and improve performance in real environments.

You will work across control theory, machine learning, and physics-based simulation, validating algorithms in simulation, lab setups, and real buildings with a focus on reliability and real-time operation.

Qualifications

• MS or PhD in control engineering, computer science, robotics, applied mathematics, mechanical engineering, or a related field.
• Expertise in AI development, scientific machine learning, optimal control, reinforcement learning, multi-agent systems, physics-informed machine learning, and optimization theory.
• Strong background in control theory, model predictive control, state estimation, and system identification.
• Strong programming skills in Python, C++, Swift, or a similar language.

Responsibilities

• Develop autonomous control strategies using 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 (e.g., stochastic gradient descent, coordinate descent, distributed optimization, Bayesian methods, evolutionary algorithms).
• Design and implement physics-based automated fault-detection and diagnosis algorithms.
• Develop fault-tolerant control methods enabling degraded operation and recovery.
• Integrate control and learning algorithms into production systems with reliability and real-time performance.

Skills

AI development

Scientific ML

Optimal control

Reinforcement learning

Multi-agent systems

Physics-informed ML

Optimization theory

Education

MS/PhD in relevant field

Tools

Python

C++

Swift

Job description

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

About PassiveLogic

PassiveLogic 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.

About the Role

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.

What You’ll Do

Develop Autonomous Control and Optimization

• 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 AI and Learning Systems

• 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.

Validate Physical AI Systems

• 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.

What You’ll Bring

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!

You Must Have

• 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 learning, multi‑agent systems, physics‑informed machine learning, and optimization theory.
• Strong technical background in control theory, model predictive control, state estimation, and system identification.
• Strong programming skills in Python, C++, Swift, or a similar language.
• Strong analytical, debugging, root‑cause analysis, communication, and collaboration skills.

You Should Have

• Experience with automatic differentiation and differentiable programming.
• Experience with software design, design patterns, and software architecture.
• Knowledge of building science, HVAC systems, thermodynamics, energy modeling, or grid‑interactive controls.

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.
• Experience in vector, SIMD, and tensor computational methods.

We know there are candidates who might not fit everything we’ve described above, or who might have experience and skills we haven’t considered. PassiveLogic can sometimes be flexible enough to shift responsibilities to the right person, or otherwise identify open or upcoming roles that may better fit your professional background. Even if you don’t meet all the requirements above, we still want to hear from you.

Compensation, Benefits & Perks

• Competitive compensation
• Generous equity package
• Medical, dental, and vision coverage
• Disability and life insurance options
• Flexible paid time off
• Team‑building events
• Free catered lunch in the office Monday through Friday
• Free National Parks pass
• Free ski pass—we’re located at the base of Big Cottonwood Canyon
• Or freeClassPass, access to fitness classes

Diversity and Inclusion

Diversity, inclusion, and belonging is woven into our values and everything we do. We welcome all—come as you are and bring your whole self. We are proud to be an Equal Opportunity Employer. We celebrate diversity every day by maintaining a safe and inclusive environment for our employees at every stage of their careers.