Graduate Student Intern - Software Engineering

Cadence Systems • Austin, TX

21h

About The Position

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 Graph Neural Networks (GNNs), to graph- or mesh-structured engineering data Assist in developing AI-driven approaches for engineering and physics-based applications, such as thermal and structural simulation Work with researchers and engineers to prototype, test, and evaluate AI models in simulation workflows Analyze experimental results and help improve model accuracy and performance Contribute to technical discussions, documentation, and research or prototype code Basic Qualifications Currently pursuing a Master’s degree or PhD in Computer Science, Engineering, or a related field Strong foundation in data structures and algorithms Programming experience in C/C++ and Python Familiarity with basic software development practices (debugging, version control) Strong collaboration skills, curiosity, and motivation to learn Preferred Qualifications Familiar with mesh-based data structures or graph representations Coursework or project experience related to AI/ML Experience using GNNs for mesh- or graph-based engineering and simulation problems Exposure to EDA, CAD/CAE, or other simulation-based domains Interest in performance optimization, parallel computing, or GPU acceleration Experience with ML frameworks (e.g., PyTorch, TensorFlow) is a plus We’re doing work that matters. Help us solve what others can’t.

Requirements

• Currently pursuing a Master’s degree or PhD in Computer Science, Engineering, or a related field
• Strong foundation in data structures and algorithms
• Programming experience in C/C++ and Python
• Familiarity with basic software development practices (debugging, version control)
• Strong collaboration skills, curiosity, and motivation to learn

Nice To Haves

• Familiar with mesh-based data structures or graph representations
• Coursework or project experience related to AI/ML
• Experience using GNNs for mesh- or graph-based engineering and simulation problems
• Exposure to EDA, CAD/CAE, or other simulation-based domains
• Interest in performance optimization, parallel computing, or GPU acceleration
• Experience with ML frameworks (e.g., PyTorch, TensorFlow) is a plus

Responsibilities

• Explore and apply AI/ML techniques, including Graph Neural Networks (GNNs), to graph- or mesh-structured engineering data
• Assist in developing AI-driven approaches for engineering and physics-based applications, such as thermal and structural simulation
• Work with researchers and engineers to prototype, test, and evaluate AI models in simulation workflows
• Analyze experimental results and help improve model accuracy and performance
• Contribute to technical discussions, documentation, and research or prototype code

What This Job Offers

• Career Level: Intern