Machine Learning Process Engineer
Core
Develop and deploy machine learning models for yield optimization, defect analysis, and process parameter tuning in semiconductor manufacturing.
Role type
Machine Learning Process Engineer
Builds
Production ML models for Automated Optical Inspection (AOI) and process optimization
Domain
Semiconductor manufacturing / Industrial IoT
Deliverable
production ML models
Required skills
Python, PyTorch, TensorFlow, Statistical Process Control (SPC), Design of Experiments (DOE), Docker, SQL, Git, SECS/GEM, MQTT
Preferred skills
CI/CD pipelines, Tableau, PowerBI, STDF, ATDF
Technologies
Python, Scikit-Learn, XGBoost, Pandas, PyTorch, TensorFlow, Docker, REST APIs, Jupyter, Git, SQL, Tableau, PowerBI, SECS/GEM, MQTT
Responsibilities
Develop ML models to identify root causes of package failures using sensor and metrology data; Enhance and deploy Computer Vision (CNN-based) models for Automated Optical Inspection (AOI); Transition models from local environments into the factory's execution system; Use regression and reinforcement learning to suggest optimal machine parameters; Build and manage data pipelines ingesting high-frequency sensor data; Design experiments to validate model performance and monitor for model drift.
Seniority
Mid-level, hands-on IC