Staff Defect Metrology AI/ML Engineer

Renesas Electronics•Goleta, CA

About The Position

Renesas is seeking a Staff Engineer, Defect Metrology to provide technical leadership in semiconductor defect inspection, data analytics, and development of advanced defect detection and classification capabilities for its MOCVD team. The role combines hands-on semiconductor metrology with Python, data analysis, image processing, and AI/ML to improve product quality, process control, and manufacturing yield.

Requirements

BS with 5+ years, MS with 3+ years, or PhD in Engineering, Science, Data Science, Computer Science, or a related field.
Experience in semiconductor defect inspection, metrology, yield engineering, manufacturing analytics, image analysis, or a related area.
Strong data analysis and programming skills, preferably with Python and SQL.
Experience applying AI/ML, computer vision, statistical learning, or advanced analytics to engineering problems.
Experience working with large datasets, including data extraction, manipulation, statistical analysis, visualization, and correlation.
Strong organizational skills, attention to detail, and demonstrated analytical and problem-solving capabilities.
Strong teamwork and collaboration skills, with the ability to work effectively across process, equipment, manufacturing, development, and data/IT functions.
Must be currently authorized to work in the United States on a full-time basis; Renesas is unable to sponsor applicants for work visas for this position.

Nice To Haves

Familiarity with tools or libraries such as pandas, NumPy, scikit-learn, OpenCV, PyTorch, or TensorFlow.
Experience with AI/ML-based wafer defect detection or classification.
Experience developing automated data pipelines, dashboards, or manufacturing analytics tools.
Experience with optical wafer inspection systems such as KLA Candela or Nanotronics.
Knowledge of semiconductor materials, epitaxial growth, and materials characterization.
Experience with DOE, SPC, process capability, and statistical modeling.
Familiarity with cleanroom protocols and semiconductor wafer handling.

Responsibilities

Develop image-processing algorithms and AI/ML models to detect, classify, and quantify semiconductor defects such as particles, pits, and scratches.
Analyze large datasets from wafer inspection, metrology, and MOCVD processes to identify trends, correlations, anomalies, and root causes.
Develop automated analysis and visualization workflows using Python, SQL, JMP, Excel, and related tools.
Correlate incoming substrate defects with epitaxial defects and develop data-driven methods for substrate quality screening.
Integrate defect, process, equipment, and wafer-level data to improve understanding of defect mechanisms and yield drivers.
Partner with process, equipment, and development engineers to correlate defect excursions with process changes and equipment events.
Improve inspection methodologies, algorithms, calibrations, statistical metrics, and defect classification strategies.
Ensure the accuracy, repeatability, and integrity of defect metrology systems and associated data.
Apply DOE, SPC, statistical modeling, and data visualization to support process improvement.
Communicate technical findings and actionable recommendations to engineering teams, management, vendors, and customers.