Principal Optimization Engineer

Carrier•Kennesaw, GA

5d•$117,500 - $234,500•Hybrid

About The Position

The Computational Engineering group within Systems & Controls COE (Carrier WHQ) has responsibility for developing and deploying model-based methods and tools for design and operation of Carrier products. This includes using mathematical models for taking product design decisions, quantification of system uncertainty, and securing efficient operation in the field. The core technology areas of the group are physical and data-driven (ML) modeling for optimization, large-scale continuous and discrete optimization, variability analysis, data analytics, machine learning, numerical and algorithm analysis, as well as understanding requirements for mathematical models to be reliably used by numerical algorithms. The group also develops and maintains computational platforms and tools for deployment to Carrier product teams. Engineers in the group work on global projects in conjunction with Carrier business units and external research organizations (universities and research institutes) to create worldwide business impact through new product and process innovations. Position Summary The candidate will provide technical leadership in the area of numerical optimization of thermo-fluid systems which are core to the Carrier business – including design and deployment of product design methods, sales tools, and supporting the development of optimization-based supervisory controls. A strong background in physics-based modeling for optimization of HVAC equipment including chillers, heat pumps and hydronic systems is therefore key. The candidate will engage with global product teams to solicit business needs and convert those into computational decision-making workflows, methods and tools to radically impact how Carrier products are designed, deployed and operated. Key targets include improving engineering effectiveness as well as developing disruptive, innovative methods for model-based design and operation of Carrier systems. The candidate will take active part in product development to support design engineers in adopting and using new methods and tools. The candidate will also work closely with other teams in Systems & Controls Center of Excellence including teams responsible for model development (to drive the development of optimization-friendly thermo-fluid models) and controls engineering (to promote the use of computational optimization strategies (MPC, RTO) as needed). In addition, the candidate will contribute to mentoring junior engineers in the group, supervision of student interns, organization of optimization trainings, and technology roadmap development.

Requirements

Master’s Degree in Engineering or Sciences
10+ years in engineering role with a strong element of model-based computation and optimization.
Willing and able to travel domestically and internationally up to 10% of the year.

Nice To Haves

PhD Degree in an Engineering discipline (applied mathematics, mechanical, control, engineering physics, or chemical engineering).
Proven ability to capture engineering design and operation problems as mathematical programming problems (NLPs and MILPs), including attention to reliable convergence of such problems.
In-depth knowledge and experience with mathematical theory (applied mathematics, numerical analysis and functional analysis), algorithmic foundations (notably existence and convergence proofs), and methods/tools for numerical optimization (SQP, interior point method, etc.) of large-scale systems.
Experience from using common algorithms/solvers for large-scale gradient-based non-linear programs, e.g., IPOPT, CONOPT, KNITRO, and WORHP, including their respective applicability to different types of problems.
Knowledge about discrete optimization formulations, methods, and algorithms is a merit.
Expertise and experience with physics-based modeling principles and best practices of thermo-fluid systems, such as vapor compression cycles or power plants.
Experience with designing and deploying real-time optimization algorithms with application to estimation and control.
Familiarity and experience with development of computational platforms and tools in Python or equivalent.
Expertise with Pyomo is a merit.
Familiarity with using HPC and cloud-based platforms for computation at scale.
Demonstrated ability to lead and work as part of a multidisciplinary team and an entrepreneurial attitude towards technological innovation in a global environment.
Self-starter who is well-organized in an international team environment, and has excellent interpersonal, leadership and communication skills.

Responsibilities

Ensure that methods and tools developed in the group impact the Carrier business through engagement in global product projects, including capture and formulation of computational problems arising in such projects that relate to “supervisory” control formulations, trade studies and implementation.
Use control theoretic tools for robustness and stability guarantees in addition to performance optimality.
Ensure that models used for system-level optimization for integrated and distributed HVAC energy systems fulfill the requirements for efficient optimization, including non-linear programming and mixed integer/discrete programming respectively.
Ensure that appropriate computational methods, tools and algorithms for designing dynamic, optimal supervisory control are based on sound mathematical foundations and are deployed to match the needs of the Carrier business.
Support development of mathematical models for thermo-fluid systems are built based on principles and best practices that secure reliable application of numerical optimization algorithms for control applications.
Ensure that appropriate machine learning and surrogate methods applicable to optimization is used in applications.
Support development and training of staff within Carrier product teams and within the Computational Engineering group; contribute to talent pipeline by supervising student internships and these