AI Enabled Device Modeling

Global Foundries is a leading full-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the world's most inspired technology companies. With a global manufacturing footprint spanning three continents, Global Foundries makes possible the technologies and systems that transform industries and give customers the power to shape their markets. For more information, visit

Global Foundries offers full-time career opportunities for recent graduates in a fast-paced, cross-functional environment. New graduates gain practical experience through technical assignments, collaboration, mentorship, and exposure to advanced semiconductor technology development.

Global Foundries is seeking a new graduate with strong artificial intelligence and machine-learning skills to develop AI-enabled methods for semiconductor device modeling. The engineer will combine device physics, measured silicon data, simulation data, and modern AI/ML techniques to accelerate model extraction, improve predictive accuracy, and create scalable modeling workflows for CMOS, cryogenic, photonic, superconducting, and other emerging devices.

• Develop and evaluate machine-learning methods for semiconductor device modeling, including parameter extraction, surrogate modeling, model calibration, and prediction of device characteristics.
• Build reproducible data pipelines that combine measured device data, compact-model simulations, TCAD results, and relevant process or test metadata.
• Train, validate, and compare AI/ML models using appropriate baselines, holdout datasets, error metrics, uncertainty analysis, and physics-based constraints.
• Apply methods such as neural networks, Gaussian processes, optimization, sensitivity analysis, and physics-informed machine learning to device-modeling problems.
• Automate model fitting, feature extraction, hyperparameter optimization, regression testing, visualization, and reporting using Python-based workflows.
• Work with device, characterization, compact-modeling, PDK, EDA, and circuit-design teams to translate technical requirements into usable AI-enabled modeling solutions.
• Document datasets, assumptions, model architecture, training methods, validation results, limitations, and recommended usage to support traceability and knowledge transfer.
• Contribute to proof-of-concept demonstrations and transition successful methods into maintainable device-modeling and design-enablement workflows.

Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs.

Work and collaborate on other projects and/or assignments as needed.

• Education
  - Bachelor's or Master's degree completed or expected in Electrical Engineering, Computer Engineering, Computer Science, Data Science, Applied Physics, Physics, or a related field from an accredited degree program.
• Academic or project experience in machine learning, deep learning, statistical modeling, optimization, or scientific data analysis.
• Strong Python programming skills and experience with common scientific and machine-learning libraries such as Num Py, pandas, Sci Py, scikit-learn, PyTorch, Tensor Flow, or JAX.
• Understanding of supervised learning, regression, neural networks, model validation, overfitting, uncertainty, and quantitative performance evaluation.
• Foundational understanding of semiconductor device physics and transistor electrical behavior, with interest in applying AI methods to device modeling.
• Experience working with structured scientific or engineering datasets, including data preparation, visualization, feature engineering, and reproducible analysis.
• Ability to communicate technical results clearly and collaborate across AI, device, modeling, characterization, and design teams.
• Travel
  - Up to 10%.
• Fluency in English - written and verbal.

• Graduate coursework, thesis research, internship, co-op, or substantial project work in AI/ML for semiconductor devices, electronic design automation, scientific machine learning, or computational physics.
• Experience with physics-informed neural networks, differentiable programming, Bayesian optimization, Gaussian processes, active learning, or uncertainty quantification.
• Exposure to compact transistor models, SPICE simulation, parameter extraction, TCAD, cryogenic device…