Sr. Power Electronics Engineer - EPiCS

• Develop and enhance Python/NumPy-based simulation frameworks for power converter and magnetics modeling. 
• Implement advanced algorithms for core loss prediction (iGSE, ML-based models) and winding loss estimation using matrix and Fourier techniques. 
• Integrate FEM workflows for magnetic component analysis and automate impedance matrix generation.
• Build parametric models of cores and windings to enable geometry-driven optimization and shape refinement. 
• Couple simulation tools with optimization algorithms to identify high-efficiency designs under real-world constraints. 
• Validate models against experimental data and continuously improve predictive accuracy. 
• Collaborate with hardware design teams to accelerate development of next-generation power conversion systems. 
• Contribute to software architecture decisions, code quality standards, and documentation for internal modeling frameworks. 

Who you are and What you bring.

• Strong proficiency in Python, with experience in scientific computing and optimization using libraries such as NumPy and SciPy. 
• Solid understanding of software development best practices, including Git version control, branching strategies, unit testing, API design, and documentation. 
• Ability to write clean, maintainable, and well-structured code for modeling and simulation workflows. 
• Experience coupling simulation frameworks with optimization algorithms for parametric and geometry-driven design. 
• Deep knowledge of power electronics fundamentals, with hands-on experience in lab measurements and validation. 
• Familiarity with magnetic component design, ferrite material properties, and core loss modeling techniques (e.g., iGSE and ML-based approaches). 
• Experience with FEM tools for electromagnetic analysis and integration into design workflows. 
• Understanding of resonant converter topologies and their unique modeling challenges; direct experience is a strong plus. 
• Comfortable working in a fast-paced R&D environment with evolving priorities and high technical complexity. 
• Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, or a related field; PhD or equivalent experience in power electronics or magnetics modeling is a plus.