Principal Optimization Engineer

Carrier

Principal Optimization Engineer developing optimization algorithms and ML-enabled tools for HVAC innovation at Carrier. Collaborating with international teams to solve complex engineering challenges.

Posted 7/23/2026full-timeHisings Backa • 🇸🇪 SwedenLead💰 SEK 648,000 - SEK 1,295,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in optimization methods and tools for thermo-fluid systems, with a strong foundation in model-based engineering and numerical optimization techniques. Proficient in developing control strategies and collaborating across teams to deliver comprehensive solutions.

Highest-signal resume keywords

Master’s Degree In EngineeringNumerical Optimization (NLP, MILP)Python (Pyomo Preferred)Thermo-Fluid Systems ModelingReal-Time Optimization And Control Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Optimization TheoryMathematical ModelingDecision-Making WorkflowsAlgorithm DesignNon-Linear Optimization TechniquesMixed-Integer Optimization TechniquesModel-Based EngineeringComputational MethodsSolver Tools (IPOPT, KNITRO)Scalable Computing Platforms

Soft Skills

MentoringCollaborationTraining Support

Tools & Technologies

Machine Learning ModelsSurrogate ModelsCloud ComputingHigh-Performance Computing (HPC)

Certifications & Qualifications

PhD In Mechanical EngineeringPhD In Control EngineeringPhD In Applied MathematicsPhD In Engineering PhysicsPhD In Chemical Engineering

Industry Keywords

Thermo-Fluid SystemsHVACVapor CompressionEnergy SystemsOptimization-Based Control Strategies

Tech Stack

Tools & technologies

CloudPython

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

Master’s degree in Engineering, Applied Mathematics, or related field
Extensive experience in model-based engineering, optimization, or computational methods
PhD in Mechanical, Control, Applied Math, Engineering Physics, or Chemical Engineering (preferred)
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)

Benefits

Comp & perks

Company pension scheme
Company contributed Share Incentive Plan
Employee Assistance Programme
You may be eligible for an annual incentive