AI enabled Device Modeling Engineer

GlobalFoundries•Essex, VT

1d•$69,000 - $120,000

About The Position

GlobalFoundries is seeking a Level 6 AI-Enabled Device Modeling Engineer to apply advanced artificial intelligence, machine-learning, and scientific-computing methods to semiconductor device-modeling challenges. Working within a cross-functional technology-development environment, the engineer will collaborate with device physicists, compact-modeling experts, characterization engineers, PDK developers, and circuit designers. The role independently develops and deploys AI-enabled methods combining device physics, measured silicon data, compact-model simulations, and TCAD results. These methods will accelerate model extraction, improve predictive accuracy, and create scalable modeling workflows for CMOS, cryogenic, photonic, superconducting, and other emerging devices. The engineer will lead proof-of-concept demonstrations and help transition effective methods into maintainable device-modeling and design-enablement workflows.

Requirements

Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Data Science, Applied Physics, Physics, or a related field.
Typically 3–5+ years of relevant experience in machine learning, scientific computing, semiconductor device modeling, computational physics, or a related field; final experience requirements will align with GF Level 6 scoping guidelines.
Strong Python programming skills and experience with scientific and machine-learning libraries such as NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or JAX.
Understanding of supervised learning, regression, neural networks, model validation, overfitting, uncertainty, and quantitative performance evaluation.
Working knowledge of semiconductor device physics and transistor electrical behavior, with demonstrated ability to apply AI methods to scientific or engineering modeling problems.
Experience developing reproducible workflows for structured scientific or engineering datasets, including data preparation, visualization, feature engineering, model training, validation, and performance monitoring.
Ability to independently execute technical assignments, communicate results clearly, and collaborate across AI, device, modeling, characterization, PDK, and design teams.
Fluency in written and spoken English.

Nice To Haves

Industry, research, or advanced academic experience applying AI/ML to 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 behavior, photonic devices, superconducting devices, or quantum hardware.
Experience using experiment tracking, version control, testing, or reproducibility tools such as Git, MLflow, Weights & Biases, containers, or automated workflows.
Experience with Linux environments, high-performance computing, GPU acceleration, cloud computing, or large-scale simulation and training datasets.
Ability to balance predictive performance with physical consistency, interpretability, robustness, and computational efficiency.
Demonstrated technical contribution through deployed software, patents, publications, conference presentations, open-source work, or interdisciplinary engineering projects.
Project management skills, including the ability to innovate, execute meaningful solutions, and navigate ambiguity.
Strong written and verbal communication, planning, and organizational skills.

Responsibilities

Develop, optimize, and validate machine-learning methods for semiconductor device modeling, including parameter extraction, surrogate modeling, model calibration, inverse modeling, and prediction of device characteristics.
Build reproducible data pipelines combining 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 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.
Collaborate 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 focused proof-of-concept demonstrations and transition successful AI/ML methods into maintainable device-modeling and design-enablement workflows with appropriate testing, documentation, and stakeholder review.
Perform all activities safely and responsibly and support Environmental, Health, Safety & Security requirements and programs.
Work and collaborate on other projects or assignments as needed.