AI-Enabled Device Modeling Engineer

Job Title

AI-Enabled Device Modeling Engineer

Role Summary

Develop and deploy AI/ML methods for semiconductor device modeling within a cross-functional technology-development team. Deliver production-relevant modeling solutions that combine device physics, measured silicon data, compact-model simulations, and TCAD results.

Collaborate with device physicists, compact-modeling experts, PDK developers, and circuit designers to accelerate model extraction, improve predictive accuracy, and create scalable modeling workflows for CMOS, cryogenic, photonic, superconducting, and other emerging devices.

Experience Level

Mid-level - typically 3–5+ years of relevant experience in machine learning, scientific computing, semiconductor device modeling, or computational physics.

Responsibilities

Key responsibilities include developing ML models and production workflows for device modeling.

• Develop, optimize, and validate ML methods for parameter extraction, surrogate modeling, model calibration, inverse modeling, and prediction of device characteristics.
• Design and maintain reproducible data pipelines that combine measured device data, compact-model simulations, TCAD results, and process/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.
• 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.
• Lead proof-of-concept demonstrations and transition successful AI/ML methods into maintainable modeling and design-enablement workflows with appropriate testing and stakeholder review.
• Perform activities in a safe and responsible manner and support Environmental, Health, Safety & Security requirements.

Requirements

Must-have skills and experience.

• 3–5+ years applying machine learning or scientific computing to engineering or scientific problems (device modeling, computational physics, or related fields).
• Strong Python programming skills and experience with scientific and ML libraries such as NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or JAX.
• Understanding of supervised learning, regression, neural networks, model validation, overfitting, uncertainty quantification, and quantitative performance evaluation.
• Working knowledge of semiconductor device physics and transistor electrical behavior and experience applying AI methods to engineering models.
• Experience developing reproducible workflows for structured scientific datasets: data preparation, visualization, feature engineering, model training, validation, and performance monitoring.
• Ability to independently execute technical assignments, communicate results clearly, and collaborate across multidisciplinary teams.
• Travel up to 10% and fluency in written and verbal English.

Nice-to-have:

• 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 behavior, photonic or superconducting devices, or quantum hardware.
• Familiarity with experiment tracking, version control, containers, MLflow, Weights & Biases, high-performance computing, GPU acceleration, or cloud training.
• Demonstrated technical contributions such as deployed software, patents, publications, or open-source work.

Education Requirements

Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Data Science, Applied Physics, Physics, or a related field.

About the Company

Company: GlobalFoundries

Headquarters: Saratoga Springs, New York, USA

GlobalFoundries is a leading contract manufacturer for the global semiconductor industry, with facilities in multiple countries, including the USA. The company develops a broad portfolio of semiconductor technologies and employs around 13,000 people worldwide. GlobalFoundries focuses on enhancing competitiveness in specialized application solutions and fostering innovation in mobile communications, consumer electronics, and automotive applications.