Applications Engineer (ML/Auto Defect Classification)

Overview

 Role Summary

We are seeking a Senior Applications Engineer to join our team, focusing on the development of cutting-edge machine learning and artificial intelligence solutions for the semiconductor industry. The ideal candidate will have extensive experience in creating robust and scalable software, with a strong background in data analysis, machine learning, and containerization technologies.

Responsibilities

• Design and Implement ML/AI Algorithms: Help develop and implement advanced machine learning and AI-based algorithms for the automatic classification of defects in semiconductor inspection tools.
• Data Analysis: Analyze large volumes of defect data to identify critical patterns, trends, and anomalies, using this analysis to inform model development.
• Training and Model Development: Train, validate, and deploy defect classification models, ensuring they meet strict performance and accuracy requirements.
• System Optimization: Continuously improves the accuracy, efficiency, and reliability of the defect classification system through iterative development and optimization.

 

Qualifications

 

• Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Materials Science, or a related technical field.
• Machine Learning Expertise: Proficiency in Python and deep learning frameworks such as TensorFlow,  PyTorch specifically for computer vision tasks (CNNs, Transformers).
• Semiconductor Knowledge: Familiarity with semiconductor manufacturing processes or inspection metrology is highly preferred.
• Data Proficiency: Experience handling large datasets and using tools like Pandas, NumPy and SQL for data preprocessing and feature engineering.
• Problem Solving: Strong analytical mindset with the ability to translate complex manufacturing defects into actionable data models, data ingestion, analysis, and visualization.

Preferred Skills

• Experience with Mismatched Data or Active Learning techniques to handle rare defect types.
• Knowledge of ML Ops tools (ML Flow, zen Flow etc.) for model deployment and monitoring in a production environment.
• Excellent communication skills to collaborate with cross-functional hardware and software teams.

Pay Range

USD $130,000.00 - USD $160,000.00 /Yr.

 

• Education: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Materials Science, or a related technical field.
• Machine Learning Expertise: Proficiency in Python and deep learning frameworks such as TensorFlow,  PyTorch specifically for computer vision tasks (CNNs, Transformers).
• Semiconductor Knowledge: Familiarity with semiconductor manufacturing processes or inspection metrology is highly preferred.
• Data Proficiency: Experience handling large datasets and using tools like Pandas, NumPy and SQL for data preprocessing and feature engineering.
• Problem Solving: Strong analytical mindset with the ability to translate complex manufacturing defects into actionable data models, data ingestion, analysis, and visualization.

Preferred Skills

• Experience with Mismatched Data or Active Learning techniques to handle rare defect types.
• Knowledge of ML Ops tools (ML Flow, zen Flow etc.) for model deployment and monitoring in a production environment.
• Excellent communication skills to collaborate with cross-functional hardware and software teams.

• Design and Implement ML/AI Algorithms: Help develop and implement advanced machine learning and AI-based algorithms for the automatic classification of defects in semiconductor inspection tools.
• Data Analysis: Analyze large volumes of defect data to identify critical patterns, trends, and anomalies, using this analysis to inform model development.
• Training and Model Development: Train, validate, and deploy defect classification models, ensuring they meet strict performance and accuracy requirements.
• System Optimization: Continuously improves the accuracy, efficiency, and reliability of the defect classification system through iterative development and optimization.