Applications Engineer (ML/Auto Defect Classification)

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

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

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Not verified

Verification

Not verified last checked Jun 13, 2026

Salary

Not verified not verified - source not recorded; timestamp not recorded

401(k) match

Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

Was this benefit information wrong? Tell us.

Schedule

Shift type

Not verified

Weekend work

Not verified

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

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

Read the full description at careers-pdf.icims.com. FewerJobs shows a source-linked preview and links to the original posting.