About Central Utility Plant Optimization

Central plants are the biggest contributor to occupant comfort, the biggest supplier of energy-and the biggest consumer of energy. Building managers can keep it running at optimum efficiency with the next generation of plant optimization software from Johnson Controls. We build on our innovative OpenBlue digital platform to connect systems and data for intelligent, automated decision-making. Our Enterprise Manager Central Utility Plant Optimization (CUPO) solution monitors thousands of variables, gathering data every 15 minutes from your connected equipment and from external sources such as weather forecasts and utility rates. CUPO automatically generates and implements optimization decisions, controlling many brands of equipment and plant types. Customers see rapid ROI, reduced costs, increased reliability, and advancement of sustainability goals.

What We Offer:

• Competitive salary
• Paid vacation/holidays/sick time
• Comprehensive benefits package including 401K, medical, dental, and vision care.
• On-the-job/cross-training opportunities
• Encouraging and collaborative team environment
• Dedication to safety through our Zero Harm policy

What you will do:

As a member of the OpenBlue AI team, the Senior Algorithm Engineer leads development and maintenance of the numerical algorithms that underpin the CUPO solution. You will improve existing algorithms to cover new equipment types and configurations or enhance optimization performance. The position will also work closely with site and modeling teams to understand reported issues, identify fixes, and resolve bugs in the algorithm code. Finally, you will contribute to development of other autonomous buildings capabilities, including optimization of airside equipment. We prefer to have this individual reside in Eastern time zone, but this is a remote opportunity.

Successful candidates will bring a strong engineering foundation and hands-on experience with numerical software development. Proficiency in MATLAB is essential, with working knowledge of Python also required. Candidates must be comfortable with reading, understanding, and debugging code written by others. Familiarity with HVAC equipment (particularly chillers), thermodynamic systems including mass/energy balances, and mathematical optimization is highly valued

How you will do it:

• Develop and maintain MATLAB and Python code to implement new CUPO algorithm features and support new equipment configurations
• Debug and resolve algorithm issues reported from live sites, working closely with field and modeling teams
• Review peer code and develop test cases to ensure algorithm correctness and quality
• Collaborate with product management to prioritize and plan development tasks, leveraging JIRA to track work and open issues
• Partner with site teams to diagnose and resolve reported issues
• Work independently to identify root causes of bugs and plan fixes
• Contribute to autonomous buildings initiatives through Python-based optimization modules
• Read and write Python code for other autonomous buildings and optimization capabilities

What you will need:

Required

• Bachelor's degree in mechanical, electrical, chemical, or other engineering field
• Familiarity with system-of-equations solvers for interconnected HVAC plant equipment
• Proficiency in MATLAB for numerical algorithm development and debugging.
• Experience with Python and scientific computing libraries (NumPy, SciPy) for data processing and algorithm implementation
• Familiarity with optimal-control strategies (e.g., dynamic programming, model-predictive control, reinforcement learning)

Preferred

• Graduate degree in Mechanical Engineering, Systems Engineering, or a related field with a focus on building energy systems, thermodynamics, or optimization
• Eight years of experience in applied engineering
• Excellent verbal and written communication skills
• Experience with Python and data-science packages (Pandas, Scikit-Learn, etc.)
• Experience reading and writing C# code
• Experience modeling HVAC equipment (chillers, cooling towers, AHUs, etc.)
• Familiarity with mass and energy balances and thermodynamics
• Familiarity with numerical optimization (e.g., mixed-integer linear/nonlinear programming)
• Proficiency in optimal-control strategies (e.g., dynamic programming, model-predictive control, reinforcement learning)
• Experience writing and debugging numerical simulations
• Experience with JIRA

SALARY RANGE: $89,000 - $149,000 (Salary to be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data.) This position includes a competitive benefits package. The posted salary range reflects the target compensation for this role. However, we recognize that exceptional candidates may bring unique skills and experiences that exceed the typical profile. If you believe your background warrants consideration beyond the stated range, we encourage you to apply. To support an efficient and fair hiring process, we may use technology assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers. For details, please visit the About Us tab on the Johnson Controls Careers site at https://jobs.johnsoncontrols.com/about-us