AI-Driven Semiconductor Device Modeling Engineer

GlobalFoundries inc.; Essex Junction (VT); On-site; USD 69,000 - 120,000; Full time

Job summary

GlobalFoundries seeks a Level 6 AI-Enabled Device Modeling Engineer to independently develop and deploy AI-enabled methods that fuse device physics, silicon data, and TCAD results for robust CMOS, cryogenic, photonic, and other devices. You will lead PoC demonstrations and transition methods into repeatable workflows.

You will collaborate with device, characterization, PDK, EDA, and circuit-design teams, translating requirements into scalable AI-powered modeling solutions with strong emphasis on

Qualifications

• 3–5+ years of relevant experience in machine learning, scientific computing, semiconductor device modeling, computational physics, or related field.
• Strong Python programming skills and experience with common scientific and ML libraries.

Responsibilities

• Develop, optimize, and validate ML 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, simulations, TCAD results, and metadata.
• Train, validate, and compare AI/ML models with baselines, datasets, and uncertainty analysis.
• Automate model fitting, feature extraction, hyperparameter optimization, and regression testing.
• Collaborate with device, characterization, PDK, EDA, and design teams to translate requirements into AI-enabled solutions.

Skills

Python

NumPy

pandas

SciPy

scikit-learn

PyTorch

TensorFlow

JAX

Gaussian processes

Bayesian optimization

Education

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

Tools

Git

MLflow

Weights & Biases

Docker

Containers

Job description

GlobalFoundries seeks a Level 6 AI-Enabled Device Modeling Engineer to independently develop and deploy AI-enabled methods that fuse device physics, silicon data, and TCAD results for robust CMOS, cryogenic, photonic, and other devices. You will lead PoC demonstrations and transition methods into repeatable workflows.

You will collaborate with device, characterization, PDK, EDA, and circuit-design teams, translating requirements into scalable AI-powered modeling solutions with strong emphasis on