Principal Software Engineer C++/Python

We are building next-generation AI-accelerated simulation technologies for Electro-Thermal, Electromagnetic, and Structural analysis. As a Software Engineer, you will take ownership of key components of the platform across the full stack, from desktop application development to AI model training and inference workflows. You will work at the intersection of scientific computing, machine learning, and 3D visualization. The main job responsibility includes:

• Design, develop, and maintain features across the C++ desktop application and Python-based APIs.
• Build and enhance scalable ML pipelines for data generation, training, evaluation, and inference.
• Develop AI-powered workflows for simulation acceleration and optimization.
• Implement and maintain cross-platform software on Linux and Windows.
• Collaborate with simulation, ML, and application engineers to deliver end-to-end solutions.
• Contribute to software architecture, code quality, testing, and performance optimization.

• MS degree or higher in Computer Science, Engineering, Applied Mathematics, or a related discipline.
• 5+ years of software development experience.
• Strong programming skills in both C++ and Python, Experience with Qt or a comparable desktop UI framework.
• Experience developing and maintaining cross-platform applications on Linux and Windows.
• Familiarity with modern machine learning frameworks such as PyTorch or TensorFlow.
• Experience with 3D geometry processing, mesh handling, or scientific/3D visualization.
• Knowledge of thermal, electromagnetic, electrical, or related engineering simulation domains.

Preferred Qualifications

• Experience developing scientific computing or CAD/EDA applications.
• Familiarity with finite element methods (FEM), computational physics, or PDE-based simulations.
• Experience deploying machine learning models into production environments.
• Knowledge of GPU programming, high-performance computing, or distributed training.
• Experience with modern software engineering practices, testing frameworks, and workflows.
• Understanding of surrogate modeling, physics-informed machine learning, or AI for engineering applications.