New Grad: AI for Semiconductor Device Modeling

GlobalFoundries inc.; Town of Malta (NY); On-site; USD 58,000 - 101,000; Full time

Job summary

GlobalFoundries is seeking a new graduate with strong AI/ML skills to develop AI-enabled methods for semiconductor device modeling across CMOS, cryogenic, photonic, and emerging devices. You will combine physics, measured data, and ML techniques to accelerate model extraction and improve predictive accuracy.

The role involves building reproducible data pipelines, training and validating models, and collaborating with device, PDK, and circuit-design teams to deliver scalable modeling workflows.

Qualifications

• Education - Bachelor's or Master's degree completed or expected in Electrical Engineering, Computer Engineering, Computer Science, Data Science, Applied Physics, Physics, or related field from an accredited 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 ML libraries (NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or JAX).
• Understanding of supervised learning, regression, neural networks, model validation, overfitting, uncertainty, and quantitative evaluation.
• Foundational semiconductor device physics knowledge and interest in AI-enabled modeling.
• Experience with structured scientific datasets, 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.

Responsibilities

• 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 metadata.
• Train, validate, and compare AI/ML models using baselines, holdout data, metrics, and physics-based constraints.
• Apply neural networks, Gaussian processes, optimization, sensitivity analysis, and physics-informed ML to device-modeling problems.
• Automate model fitting, feature extraction, hyperparameter optimization, regression testing, visualization, and reporting with Python workflows.
• Collaborate with device, characterization, compact-modeling, PDK, EDA, and circuit-design teams to translate requirements into AI-enabled modeling solutions.
• Document datasets, assumptions, model architecture, training methods, validation, limitations, and usage for traceability and knowledge transfer.
• Contribute to proof-of-concept and transition methods into maintainable device-modeling workflows.

Skills

Machine learning

Python

NumPy

pandas

SciPy

scikit-learn

PyTorch

TensorFlow

JAX

English communication

Education

Bachelor's or Master's in Electrical Engineering, Computer Engineering, Computer Science, Data Science, Applied Physics, Physics

Tools

Git

MLflow

Weights & Biases

Containers

ML tooling

Job description

GlobalFoundries is seeking a new graduate with strong AI/ML skills to develop AI-enabled methods for semiconductor device modeling across CMOS, cryogenic, photonic, and emerging devices. You will combine physics, measured data, and ML techniques to accelerate model extraction and improve predictive accuracy.

The role involves building reproducible data pipelines, training and validating models, and collaborating with device, PDK, and circuit-design teams to deliver scalable modeling workflows.