Senior Engineer, HIG-HBM Product Engineering (Media Health Reliability)

About the role

Micron is hiring a Senior Engineer for its HIG-HBM product engineering team, focused on media health reliability. The role covers reliability test flow development, qualification execution, root cause analysis of device issues, process conversion recommendations for HBM products, and the use of AI and machine learning in reliability work.

Key responsibilities

• Define and develop reliability stress test flows, test plans and qualification plans, working with the Global Quality team and driving resolution of qualification gating issues.
• Reduce extrinsic (time 0 and field) and intrinsic DPM through optimized manufacturing test flows and test strategy, meeting quality, cost and cycle time KPIs.
• Debug and resolve root causes of qualification and RMA device defects using electric failure analysis (EFA) and physical failure analysis (PFA).
• Advise fab teams on process conversions that improve yield, quality and reliability, and manage the associated risks with Product Managers/Leads.
• Promote innovation and make final decisions on risk analysis and project prioritization, collaborating with Fab, HBM Technology Development, HBM Design, System Development and Quality/Reliability teams.
• Identify high-leverage AI use cases for product qualification and apply machine learning (regression, classification, anomaly detection, clustering) to reliability data.

Required profile

• Bachelor's or Master's degree in Electrical and Electronics Engineering or a related field.
• 5+ years in the semiconductor industry with product, testing or reliability experience.
• Coursework or internship in semiconductor devices, circuit design or product validation is a plus.
• Clear written and spoken English, teamwork and strong attention to detail.

Required skills

• Solid understanding of problem-solving and root cause analysis.
• Familiarity with CMOS technology, DRAM architecture and basic device physics.
• Data analysis, statistics and scripting tools such as Python or JMP.
• Ability to apply machine learning and AI tools to reliability datasets.