Applications Engineer (ML/Auto Defect Classification)

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

$130K-$130K not verified - source not recorded; timestamp not recorded

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

16% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Applications Engineer (ML/Auto Defect Classification) Milpitas, California 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

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