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.