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Applications Engineer (ML/Auto Defect Classification)
Overview:
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:
Qualifications:
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.
- 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.
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