Senior Engineer, STPG PE NAND (NAND Reliability Product Engineer)

About the role

Micron is hiring a NAND Reliability Product Engineer to define, assess and safeguard the reliability of its NAND Flash products through advanced analytics, large-scale data infrastructure, AI and engineering innovation. The role sits at the intersection of semiconductor technology, reliability engineering, cloud computing and AI/ML, working closely with AI, IT and Quality teams to strengthen reliability risk assessment and prevent customer escapes.

Key responsibilities

• Drive development of cloud-based data platforms supporting reliability monitoring, engineering analysis and product disposition workflows.
• Design, develop and maintain scalable ETL pipelines that ingest, validate, transform and manage engineering and manufacturing data from multiple sources.
• Use Python, SQL, BigQuery and PySpark to process large datasets, perform deep analysis, generate visualizations and deliver engineering insights.
• Build and deploy AI/ML solutions for engineering decision-making, failure analysis, anomaly detection and reliability assessments.
• Apply data cleansing, outlier detection and missing data handling in distributed or large-scale frameworks.
• Support deployment, monitoring and operationalization of AI/ML solutions in cloud and enterprise environments.
• Identify opportunities where GenAI, machine learning and analytics improve engineering productivity, and promote adoption of AI-enabled solutions.
• Support customer inquiries and communicate technical findings and model outcomes to technical and non-technical stakeholders.

Required profile

• Bachelor's degree with more than 5 years, or Master's degree with more than 3 years, in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence or a related discipline.
• Strong understanding of data analytics, AI/ML or related technical fields.
• Knowledge of semiconductor reliability fundamentals, NAND technology, reliability engineering and electrical failure analysis.
• Demonstrated ownership, accountability and ability to work in cross-functional teams.

Required skills

• Strong programming proficiency in Python and SQL.
• Understanding of data engineering concepts such as data cleansing, outlier treatment and missing data handling.
• Desirable: data analytics and visualization tools such as pandas, scikit-learn, matplotlib and plotly.
• Desirable: hands-on experience building and deploying AI/ML systems with LLMs, RAG, agentic workflows, PyTorch or TensorFlow.
• Desirable: familiarity with AI coding tools such as Claude Code, Cursor, Cline, Windsurf or Gemini CLI.
• Desirable: cloud experience with GCP, AWS or Azure, including deploying ML pipelines in production.