Principal Optimization Engineer

Join Carrier’s Computational Engineering team within the Systems & Controls Center of Excellence in Lund, Sweden and help shape the future of HVAC innovation.

We develop advanced model-based methods, optimization algorithms, and ML-enabled tools that power smarter product design, improved system performance, and efficient real-world operation. Our work directly impacts global products by turning complex engineering challenges into scalable, data-driven solutions.

You’ll collaborate with international product teams, research institutions, and cross-functional experts to bring cutting-edge optimization and control strategies into real-world applications.

Your key responsibilities will be the following

•Design and deploy optimization methods and tools for thermo-fluid systems (chillers, heat pumps, hydronic systems)

• Translate business and engineering needs into mathematical models and decision-making workflows

• Develop and implement optimization-based control strategies (e.g., MPC, real-time optimization)

•Partner with global product teams to embed advanced tools into product development and operation

•Ensure robust, scalable approaches using non-linear and mixed-integer optimization techniques

• Drive adoption of ML/surrogate models in optimization workflows

•Collaborate across modeling, controls, and platform teams to deliver end-to-end solutions

•Mentor engineers, support training initiatives, and contribute to technology roadmap development

Required

• Master’s degree in Engineering, Applied Mathematics, or related field

•Extensive experience in model-based engineering, optimization, or computational methods

Preferred

• PhD in Mechanical, Control, Applied Math, Engineering Physics, or Chemical Engineering

•Considerable experience in numerical optimization (NLP, MILP) and solver tools (e.g., IPOPT, KNITRO)

•Deep understanding of optimization theory, numerical methods, and algorithm design

•Experience with thermo-fluid systems modeling (HVAC, vapor compression, energy systems)

•Experience with real-time optimization and control systems

•Proficiency in Python (Pyomo preferred) and scalable computing platforms (cloud/HPC)

•Proven ability to lead in multidisciplinary, global environments