Graduate Student Intern - Software Engineering

## Graduate Student Intern - Software EngineeringApply: AUSTIN: Part time: Posted Today: R56405## **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.
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