SW Applications Engineer

Role Overview

As a SW Applications Engineer at Applied Materials within the Process Diagnostics and Control (PDC) group, you will work with advanced imaging, image processing, and AI algorithms to detect and classify nanometer-size defects in semiconductor fabrication. You will play a central role in shaping data products and driving their success in the field by bridging the gap between R&D and customer applications.

Responsibilities

• Maximize the performance of Data Products through deep understanding of customer use-cases and collaboration with R&D and Algorithms teams.
• Drive adoption and integrated deployment of Data Product solutions at leading semiconductor fabs worldwide.
• Serve as the technical escalation point for complex field issues, providing rapid resolution and root cause analysis.
• Develop utilities and scripts using Python for debugging, workflow automation, and issue isolation.
• Conceptualize and prototype technical flows that convert multi-source data into new applications and insights.
• Translate Voice of Customer (VoC) into concrete product requirements and roadmap inputs.
• Explore and validate AI/ML-based approaches to advance data product capabilities.

Requirements

• Master’s or Bachelor’s degree in Electronics Engineering, Computer Science, VLSI, or a related field.
• 2–5 years of experience in semiconductor process and defectivity, with knowledge of CAD and design-based inspection.
• Proficiency in Python for building scripts, tools, or analytical workflows.
• Familiarity with AI/ML concepts and practical application to data analysis.
• Comfort with GenAI-assisted development tools (e.g., Claude, Copilot).
• Willingness to travel approximately 50% to leading global fabs.
• Strong analytical, problem-solving, and communication skills.

Skills

• Python
• AI/ML
• CAD
• VLSI
• GenAI

What We Offer

• Opportunity to work on cutting-edge semiconductor technology and products.
• Direct collaboration with customers and global R&D teams.
• A role with real product influence where field learnings shape the roadmap.
• Dual career track: technical specialist or engineering leadership.
• A culture of ownership, innovation, and continuous learning.