Senior Engineer: AI & Multiphysics for DRAM Models

Relha LLC; Boise (ID); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Medical plans

Dental plans

Vision plans

Paid time off

Paid holidays

Job summary

Micron Technology in Boise, Idaho is seeking a Senior Engineer on the Technology-AMAIS team to deliver model-driven solutions for DRAM development. You will lead composite models for DRAM modules, partner with process and PI teams, and provide predictive modeling insights to engineering organizations.

Responsibilities include developing physics-based and data-informed models, communicating findings to engineering leaders, integrating ML approaches, training models on sparse data, and driving

Qualifications

• Bachelor’s degree in Engineering, Artificial Intelligence, Data Science, Applied Mathematics/Sciences, or related field.
• Advanced multiscale structural modeling expertise.
• Strong numerical methods, HPC and ML/AI foundations.
• Proficient in Linux, C++, Python; cloud or HPC experience.

Responsibilities

• Develop composite models grounded in physics to enable module co-optimization across wafer- and die-level metrics.
• Extract insights from predictive models and datasets, communicating findings to engineering teams.
• Collaborate to integrate ML/agentic solutions into models meeting engineering needs.
• Train robust models on sparse data using AI techniques.
• Drive model-informed process integration strategies to improve yield.

Skills

C++

Python

ML/AI

Linux

HPC

Multiphysics

Communication

Cloud

Education

Bachelor’s degree in Engineering or related field

MS or PhD in related field

Tools

Git

Cloud platforms

Job description

Micron Technology in Boise, Idaho is seeking a Senior Engineer on the Technology-AMAIS team to deliver model-driven solutions for DRAM development. You will lead composite models for DRAM modules, partner with process and PI teams, and provide predictive modeling insights to engineering organizations.

Responsibilities include developing physics-based and data-informed models, communicating findings to engineering leaders, integrating ML approaches, training models on sparse data, and driving