Graduate Student Intern - Software Engineering
Cadence · Austin, United States · 5d ago
InternshipOn-siteSoftware 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.