{"schemaVersion":"jobsearcher.job.v1","id":"5cc21e8d36020909202fd68c","url":"https://jobsearcher.com/jobs/5cc21e8d36020909202fd68c","canonicalUrl":"https://jobsearcher.com/jobs/5cc21e8d36020909202fd68c","title":"Physical AI Engineer (Control, Optimization, Validation)","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.\n\nAbout PassiveLogic\n\nPassiveLogic 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.\n\nWe 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.\n\nAbout the Role\n\nYou will develop and validate the algorithms that enable buildings to understand their physical state, learn from experience, and autonomously optimize their operation.\n\nThis 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.\n\nWhat You’ll Do\n\nDevelop Autonomous Control and Optimization\n\nDesign and implement autonomous control strategies that use physics-based digital twins for prediction, optimization, and decision-making.\n\nTranslate comfort, energy, equipment life, and operational requirements into control objectives, constraints, and cost functions.\n\nDevelop scalable optimization algorithms using methods such as stochastic gradient descent, coordinate descent, distributed optimization, Bayesian methods, and evolutionary algorithms.\n\nDesign and implement physics-based automated fault-detection and diagnosis algorithms.\n\nDevelop fault-tolerant control methods that support degraded operation and system recovery.\n\nIntegrate control and learning algorithms into production systems while meeting reliability and real-time performance requirements.\n\nDevelop AI and Learning Systems\n\nDevelop physics-informed predictive and learning models using deep learning, reinforcement learning, and transfer learning.\n\nDevelop autonomous agents, state-estimation methods, model adaptation, and control-correction algorithms.\n\nCreate learning methods that remain reliable under noise, outliers, sparse data, model uncertainty, and changing system behavior.\n\nValidate Physical AI Systems\n\nDefine performance metrics, acceptance criteria, and automated tests for autonomous capabilities and levels of autonomy.\n\nTest system behavior under disturbances, incomplete observations, model divergence, and equipment, sensor, or communication failures.\n\nVerify fault-detection accuracy and validate fault-tolerant control, degraded operation, and recovery behavior.\n\nCompare simulation results with analytical solutions, laboratory measurements, and real-building data.\n\nWhat You’ll Bring\n\nIf 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!\n\nYou Must Have\n\nMS or PhD in control engineering, computer science, robotics, applied mathematics, mechanical engineering, or a related field.\n\nDemonstrated expertise in AI development, scientific machine learning, optimal control, reinforcement learning, multi-agent systems, physics-informed machine learning, and optimization theory.\n\nStrong technical background in control theory, model predictive control, state estimation, and system identification.\n\nStrong programming skills in Python, C++, Swift, or a similar language.\n\nStrong analytical, debugging, root-cause analysis, communication, and collaboration skills.\n\nYou Should Have\n\nExperience with automatic differentiation and differentiable programming.\n\nExperience with software design, design patterns, and software architecture.\n\nKnowledge of building science, HVAC systems, thermodynamics, energy modeling, or grid-interactive controls.\n\nIt’s Helpful to Have\n\nExperience with software-in-the-loop and hardware-in-the-loop testing.\n\nExperience with formal methods, probabilistic modeling, and graph neural network.\n\nExperience with building energy-modeling tools such as Dymola and EnergyPlus.\n\nExperience in vector, SIMD, and tensor computational methods.\n\nWe 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.\n\nCompensation, Benefits & Perks\n\nCompetitive compensation\n\nGenerous equity share package\n\nMedical, dental and vision coverage\n\nDisability and life Insurance options\n\nFlex PTO\n\nTeam-building events\n\nFree catered lunch in the office Monday — Friday\n\nFree ski pass (We are at the base of Big Cottonwood Canyon)\n\nFree National Park pass\n\nOnsite Gym\n\nWhen Applying, Include\n\nA cover letter\n\nA resume\n\nExtra mile — include a description of a project (of any type) you personally created, devised, built, managed, organized, or designed that was of your own self-initiative\n\nDiversity and Inclusion\n\nDiversity, 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.","company":"Passivelogic","rawCompany":"passivelogic","city":"Holladay","state":"UT","isRemote":false,"isActive":false,"createdAt":"2026-09-14T09:57:19.669Z","occupations":[{"code":"17-2199.05","title":"Mechatronics Engineers","slug":"mechatronics-engineers"},{"code":"17-2199.08","title":"Robotics Engineers","slug":"robotics-engineers"},{"code":"17-2141.00","title":"Mechanical Engineers","slug":"mechanical-engineers"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541330","title":"Engineering Services","slug":"engineering-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Physical AI Engineer (Control, Optimization, Validation)","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.\n\nAbout PassiveLogic\n\nPassiveLogic 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.\n\nWe 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.\n\nAbout the Role\n\nYou will develop and validate the algorithms that enable buildings to understand their physical state, learn from experience, and autonomously optimize their operation.\n\nThis 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.\n\nWhat You’ll Do\n\nDevelop Autonomous Control and Optimization\n\nDesign and implement autonomous control strategies that use physics-based digital twins for prediction, optimization, and decision-making.\n\nTranslate comfort, energy, equipment life, and operational requirements into control objectives, constraints, and cost functions.\n\nDevelop scalable optimization algorithms using methods such as stochastic gradient descent, coordinate descent, distributed optimization, Bayesian methods, and evolutionary algorithms.\n\nDesign and implement physics-based automated fault-detection and diagnosis algorithms.\n\nDevelop fault-tolerant control methods that support degraded operation and system recovery.\n\nIntegrate control and learning algorithms into production systems while meeting reliability and real-time performance requirements.\n\nDevelop AI and Learning Systems\n\nDevelop physics-informed predictive and learning models using deep learning, reinforcement learning, and transfer learning.\n\nDevelop autonomous agents, state-estimation methods, model adaptation, and control-correction algorithms.\n\nCreate learning methods that remain reliable under noise, outliers, sparse data, model uncertainty, and changing system behavior.\n\nValidate Physical AI Systems\n\nDefine performance metrics, acceptance criteria, and automated tests for autonomous capabilities and levels of autonomy.\n\nTest system behavior under disturbances, incomplete observations, model divergence, and equipment, sensor, or communication failures.\n\nVerify fault-detection accuracy and validate fault-tolerant control, degraded operation, and recovery behavior.\n\nCompare simulation results with analytical solutions, laboratory measurements, and real-building data.\n\nWhat You’ll Bring\n\nIf 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!\n\nYou Must Have\n\nMS or PhD in control engineering, computer science, robotics, applied mathematics, mechanical engineering, or a related field.\n\nDemonstrated expertise in AI development, scientific machine learning, optimal control, reinforcement learning, multi-agent systems, physics-informed machine learning, and optimization theory.\n\nStrong technical background in control theory, model predictive control, state estimation, and system identification.\n\nStrong programming skills in Python, C++, Swift, or a similar language.\n\nStrong analytical, debugging, root-cause analysis, communication, and collaboration skills.\n\nYou Should Have\n\nExperience with automatic differentiation and differentiable programming.\n\nExperience with software design, design patterns, and software architecture.\n\nKnowledge of building science, HVAC systems, thermodynamics, energy modeling, or grid-interactive controls.\n\nIt’s Helpful to Have\n\nExperience with software-in-the-loop and hardware-in-the-loop testing.\n\nExperience with formal methods, probabilistic modeling, and graph neural network.\n\nExperience with building energy-modeling tools such as Dymola and EnergyPlus.\n\nExperience in vector, SIMD, and tensor computational methods.\n\nWe 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.\n\nCompensation, Benefits & Perks\n\nCompetitive compensation\n\nGenerous equity share package\n\nMedical, dental and vision coverage\n\nDisability and life Insurance options\n\nFlex PTO\n\nTeam-building events\n\nFree catered lunch in the office Monday — Friday\n\nFree ski pass (We are at the base of Big Cottonwood Canyon)\n\nFree National Park pass\n\nOnsite Gym\n\nWhen Applying, Include\n\nA cover letter\n\nA resume\n\nExtra mile — include a description of a project (of any type) you personally created, devised, built, managed, organized, or designed that was of your own self-initiative\n\nDiversity and Inclusion\n\nDiversity, 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.","datePosted":"2026-09-14T09:57:19.669Z","dateModified":"2026-09-14T09:57:19.669Z","hiringOrganization":{"@type":"Organization","name":"Passivelogic","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Holladay","addressRegion":"UT","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"5cc21e8d36020909202fd68c"},"url":"https://jobsearcher.com/jobs/5cc21e8d36020909202fd68c"}}