Principal/Staff ML/AI Engineer, Product Engineering

About the role

Micron's Product Engineering team is looking for a Principal/Staff ML/AI Engineer to develop and deploy AI and machine learning solutions that transform semiconductor manufacturing and engineering workflows. The role builds predictive analytics models, develops agentic AI solutions, enables intelligent automation and drives data-driven decisions to improve yield, quality, cycle time, reliability and cost efficiency. It also mentors and enables Citizen Data Scientists.

Key responsibilities

• Develop, validate and deploy ML models for yield prediction, test time optimization, reliability analysis and manufacturing process improvement
• Build scalable ML workflows and agentic AI solutions for yield analysis, test program validation, root cause investigation, engineering reports and process optimization
• Integrate AI agents with enterprise platforms, collaboration tools and business systems
• Partner with engineering teams to prepare large volumes of manufacturing, test, probe and inline process data, and build data pipelines and frameworks for AI and ML
• Mentor Citizen Data Scientists, create training materials and workshops, and review AI/ML projects for technical guidance
• Drive innovation initiatives that reduce test time, improve yield and automate manual processes, and contribute to patents, publications and engineering standards

Required profile

• PhD with at least 2 years, or Master's with at least 5 years, or Bachelor's with at least 7 years of relevant experience
• At least 6 years of industry experience in machine learning, data science or AI solution development
• Proven experience delivering ML solutions in production and leading technical initiatives across multidisciplinary teams

Required skills

• Python and libraries such as Pandas, NumPy and Scikit-learn
• Supervised and unsupervised learning, regression, decision trees, random forests, gradient boosting and predictive modeling
• Large-scale datasets, end-to-end ML workflows, statistical analysis, feature engineering and model evaluation
• Version control and software development best practices
• Desirable: TensorFlow, PyTorch, LangChain, MLOps, Azure Machine Learning, AWS SageMaker, Power Automate, SharePoint, JIRA, Confluence