Senior Software Applications Engineer (AI/ML)

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 $180,000.00 /Yr.

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.

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.