The role involves delivering model-informed solutions to technology development groups focused on DRAM. The engineer will develop composite models for DRAM modules and provide predictive modeling insights to process and product engineering teams. Responsibilities include applying algorithms to train robust models with sparse datasets and designing smart experiments to accelerate process learning. The position requires integrating modeling workflows with factory data systems to enable closed-loop learning. Additionally, the engineer will identify insights from data and images to communicate findings to engineering teams for yield improvement.

Candidates must hold a Bachelor's degree with at least five years of experience, a Master's degree with at least three years of experience, or a PhD in a relevant field such as Engineering, Artificial Intelligence, or Data Science. Applicants are required to possess a strong foundation in statistics, numerical methods, and scientific computing. Demonstrated experience in statistical modeling, ML-based process modeling, and yield analytics is necessary. Furthermore, the role requires strong verbal and written communication skills to translate complex technical results into actionable insights.