Graduate Student Intern - Software Engineering at Cadence in AUSTIN

Graduate Student Intern - Software Engineering

Location: AUSTIN

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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