Autonomous Building AI Engineer

PassiveLogic; Salt Lake City (UT); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Equity package

Medical, dental and vision coverage

Disability and life insurance

Flex PTO

Team-building events

Free catered lunch in the office

Free ski pass

Free National Park pass

Onsite Gym

Job summary

PassiveLogic is hiring a Physical AI Engineer to develop and validate autonomous control algorithms for building systems. You will work on physics-informed models, digital twins, and learning-based optimization to improve comfort, energy use, and equipment life in real buildings.

The role blends control theory, ML, and system validation across simulation, lab setups, and field deployments, targeting scalable, production-ready solutions.

Qualifications

• MS or PhD in a relevant field such as control engineering, CS, robotics or applied math.
• Expertise in AI development, scientific ML, optimal control, RL, and optimization theory.
• Strong background in control theory, MPC, state estimation, and system identification.
• Proficient programming in Python, C++, Swift; strong analytical and collaborative skills.

Responsibilities

• Design autonomous control strategies using physics-based digital twins for prediction and optimization.
• Translate requirements into objectives, constraints, and cost functions for control systems.
• Develop scalable optimization algorithms and fault-detection/diagnosis methods.
• Integrate control and learning algorithms into production systems with real-time considerations.
• Validate performance across simulations, lab environments, and real buildings.

Skills

AI development

Control theory

State estimation

Python

C++

Swift

Analytical skills

Collaboration

Education

MS or PhD in control engineering or related

Tools

Dymola

EnergyPlus

Software-in-the-loop

Hardware-in-the-loop

Job description

PassiveLogic is hiring a Physical AI Engineer to develop and validate autonomous control algorithms for building systems. You will work on physics-informed models, digital twins, and learning-based optimization to improve comfort, energy use, and equipment life in real buildings.

The role blends control theory, ML, and system validation across simulation, lab setups, and field deployments, targeting scalable, production-ready solutions.