Graduate Student Intern - Software Engineering

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology. Responsibilities • Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data. • Research and develop AI-driven approaches for geometry modeling, mesh generation, and topology optimization workflows. • Prototype and evaluate AI-assisted methods for automating geometry creation and simulation model preparation. • Work with researchers and engineers to integrate AI technologies into engineering and physics-based applications, including thermal and structural simulation. • Analyze experimental results and improve the quality, robustness, and performance of AI-generated geometry and mesh models. • Investigate methods to reduce manual modeling effort and accelerate design and simulation workflows through AI automation. • Contribute to technical discussions, documentation, research reports, and prototype software development. Basic Qualifications • Currently pursuing a Master's degree or PhD in Computer Science, Engineering, Applied Mathematics, or a related field. • Strong foundation in data structures, algorithms, and software engineering principles. • Programming experience in C/C++ and Python. • Familiarity with software development practices, including debugging, testing, and version control. • Strong analytical, problem-solving, collaboration, and communication skills. • Curiosity and enthusiasm for applying AI technologies to engineering problems. Preferred Qualifications • Experience with AI/ML, including deep learning, LLMs, GenAI, or GNNs. • Familiarity with geometric modeling, mesh generation, retopology, computational geometry, or graph-based representations. • Coursework or research experience in computer graphics, computer-aided engineering (CAE), scientific computing, or simulation. • Exposure to CAD, CAE, EDA, or simulation-driven design applications. • Interest in topology optimization, geometry processing, performance optimization, parallel computing, or GPU acceleration. • Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar tools. We’re doing work that matters. Help us solve what others can’t.