Sr. Staff R&D Engineer, OPC & ILT Machine Learning Modeling

General Information

Job Title Sr. Staff R&D Engineer, OPC & ILT Machine Learning Modeling Job ID 16533 City Sunnyvale State/Province California Date Posted 20-Mar-2026 Job Category Engineering Job Subcategory R&D Engineering Hire Type Employee Remote Eligible No Base Salary Range: $165000 - $248000

Descriptions & Requirements

Job Description and Requirements

You Are:

You are an accomplished and innovative engineer with a deep passion for tackling complex, physics-driven challenges in high-performance software environments. Your expertise in Python and C++ stands out, alongside your proficiency in parallel computing, statistical analysis, and optimization. Collaborative by nature, you communicate effectively and enjoy working with diverse, cross-functional teams, sharing knowledge and learning from others. You are motivated by the prospect of continuous learning and growth, and you embrace new technologies such as machine learning, computational lithography, and advanced image processing. Your drive for excellence, adaptability, and enthusiasm for innovation make you a critical contributor to complex engineering projects. If you are looking to make a tangible impact in the world of semiconductor software, and you enjoy solving hard problems while modernizing infrastructure, this opportunity is designed for you.

What You’ll Be Doing:

• Maintaining and improving existing functionalities within advanced modeling software platforms.
• Understanding and translating technical requirements from customers into robust model solutions.
• Designing innovative, physics-driven model solutions by analyzing measurement data and proposing sophisticated algorithms.
• Creating detailed specifications and comprehensive test plans for proposed model solutions.
• Collaborating with cross-functional teams—including software, hardware, and customer-facing groups—to ensure seamless solution integration.
• Applying modern computational techniques to optimize performance and scalability of modeling componen