AI-Enabled Device Modeling (AI/ML)

Location: Malta, New York, USA; travel up to 10%

Expected salary: $58,400.00–$100,800.00 per year

Company

hirify.global is a full-service semiconductor foundry providing design, development, and fabrication services with a global manufacturing footprint.

What you will do

• Develop and evaluate machine-learning methods for semiconductor device modeling, including parameter extraction, surrogate modeling, calibration, and device-characteristic prediction.
• Build reproducible pipelines combining measured device data, compact-model simulations, TCAD results, and process or test metadata.
• Train, validate, and compare AI/ML models using baselines, holdout datasets, error metrics, uncertainty analysis, and physics-based constraints.
• Automate model fitting, feature extraction, hyperparameter optimization, regression testing, visualization, and reporting with Python.
• Collaborate with device, characterization, compact-modeling, PDK, EDA, and circuit-design teams to create usable modeling solutions.
• Document methods and results, contribute to proof-of-concept demonstrations, and transition successful approaches into maintainable workflows.

Requirements

• 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 program.
• Academic or project experience in machine learning, deep learning, statistical modeling, optimization, or scientific data analysis.
• Strong Python skills and experience with scientific or 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.
• Foundational understanding of semiconductor device physics and transistor electrical behavior, with interest in applying AI to device modeling.
• Experience with structured scientific or engineering datasets, including preparation, visualization, feature engineering, and reproducible analysis.
• Ability to communicate technical results clearly and collaborate across AI, device, modeling, characterization, and design teams.
• Fluency in written and verbal English; willingness to travel up to 10%.

Nice to have

• Graduate coursework, thesis research, internship, co-op, or substantial project work in AI/ML for semiconductor devices, EDA, 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, parameter extraction, TCAD, cryogenic or photonic devices, superconducting devices, or quantum hardware.
• Experience with experiment tracking, version control, testing, reproducibility tools, Linux, high-performance computing, GPU acceleration, cloud computing, or large-scale datasets.
• Ability to balance predictive performance with physical consistency, interpretability, robustness, and computational efficiency.
• Publications, conference presentations, open-source contributions, competitions, interdisciplinary research, project management, innovation, and strong planning and organizational skills.

Culture & Benefits

• Full-time opportunity for a recent graduate in a fast-paced, cross-functional environment.
• Practical experience through technical assignments, collaboration, mentorship, and exposure to advanced semiconductor technology development.
• Work is subject to Environmental, Health, Safety & Security requirements and programs.
• Employment is contingent on successful completion of applicable pre-employment conditions and follows equal-opportunity policies.

Hiring process

• An offer is conditioned upon successful completion of applicable pre-employment conditions and compliance with relevant laws and regulations.
• Reasonable-accommodation requests during the employment process must be submitted by email with the nature of the request and contact information.