Principal Product Engineer, (AI/ML & Advanced Data Analytics Lead), Heterogeneous Integration Group(HIG), High Bandwidth Memory (HBM)

Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

As part of the HIG HBM Product and System Engineering organization, you will be leading a team of engineers building the predictive models that determine how HBM is screened, dispositioned, and tested in high-volume manufacturing.

In this role, you will work on intelligent systems that improve engineering productivity, strengthen technical decision-making, and unlock insights from complex manufacturing, validation, and engineering workflows.

You will collaborate with cross-functional teams across Product and System Engineering, Design Engineering, Test Engineering, Data Science, IT, and Manufacturing to prototype, build, and scale practical AI-driven solutions that improve quality, cost, cycle time, and engineering efficiency. Also, you will need to be experienced as people leader and manager and able to lead, manage and coach a group of engineers.

Key Responsibilities

• Mentorship and Development: Actively mentor and develop team members to foster growth and development within the team and the organization.

• Team and Technical Leadership: Lead a team of engineers, set technical direction, review modeling work with rigor, balance workload across a shifting portfolio, and hire and onboard to raise the technical bar of the team.

• Predictive Yield and Reliability Modeling: Own the design, training, validation, and productization of models that predict yield loss, defect escapes, and reliability risk from manufacturing and test data, applying gradient-boosted tree methods, time-series and temporal models, anomaly detection, and classical statistical methods.

• Model Lifecycle and Drift Control: Build and operate the deployment path, including real-time and batch inference, result storage, performance monitoring, drift detection, retraining triggers, feature change management, and rollback, ensuring training and production feature pipelines are provably identical.

• Machine Learning Production: Develop and productionize machine learning and deep learning models for classification, regression, anomaly detection, failure analysis, and engineering decision support.

• Large-Scale Data Pipelines: Develop scalable data pipelines and analytical workflows to ingest, clean, transform, and analyze large, complex, and heterogeneous datasets from multiple manufacturing and engineering systems.

• Advanced Data Analytics: Apply Python, SQL, and data science libraries (e.g., pandas, matplotlib) to perform deep analysis, generate visualizations, and deliver actionable engineering insights.

• Distributed Data Processing: Implement robust data processing techniques such as data cleansing, outlier detection, and missing-data handling using distributed or large-scale frameworks (e.g., PySpark, BigQuery).

• Production Deployment: Support deployment, monitoring, and operationalization of AI/ML solutions in cloud and enterprise environments.

• GenAI and Agentic Augmentation: Apply GenAI and agentic systems to failure triage, root-cause analysis, engineering knowledge retrieval, data extraction, code generation, and analysis automation, and use modern AI coding tools to raise team throughput.

• Cross-Functional Collaboration: Partner with domain experts and cross-functional teams to translate complex engineering problems into scalable AI/ML and analytics solutions, and align with Product, Test, Yield, Process, and Fab teams on fail-mode definitions, screening conditions, disposition changes, and deployment decision-making.

• Technical Communication: Communicate technical findings, recommendations, and model outcomes clearly to both technical and non-technical stakeholders.

• Innovation Leadership: Identify and drive high-impact opportunities where machine learning, advanced analytics, and GenAI can improve yield, quality, cost, cycle time, and engineering productivity.

Minimum Qualifications

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence, or a related field.

• Demonstrating Strong Leadership Skills and Technical Skills, especially in problem solving with root cause understanding and solution space.

• Dedicated and highly motivated with a flexible approach towards adapting to different roles in a dynamic working environment (from leading the team to leading technical programs).

• Minimum 5 years of hands-on machine learning, data science, or predictive-modeling experience, including at least 2 years leading a team of engineers or data scientists as a people manager or formal technical lead, setting direction, reviewing others' technical work, and developing team members.

• Demonstrated depth in gradient-boosted tree methods such as XGBoost, LightGBM, or CatBoost applied to real production problems, including feature engineering, severe class imbalance, hyperparameter tuning, probability calibration, threshold selection, and interpretability.

• Practical experience with time-series and temporal modeling, including forecasting, trend and change-point detection, rolling-window feature construction, temporal cross-validation, and distribution-drift detection.

• Track record of models running in production rather than only in notebooks, including deployment, performance monitoring, and retraining, with a clear account of at least one model whose live behavior diverged from offline results and what was done about it.

• Ability to define and defend business-level acceptance criteria for a model, translating model output into cost, yield, quality, or cycle-time terms that a manufacturing organization will act on.

• Strong programming proficiency in Python and SQL.

• Strong technical foundation in data analytics and visualization, including tools and libraries such as pandas, scikit-learn, matplotlib, plotly, or similar ecosystems.

• Experience analyzing large, complex, and heterogeneous datasets from multiple systems and applying sound techniques for data cleansing, outlier handling, and missing-data treatment.

• Cloud experience with GCP, AWS, or Azure, including deploying ML pipelines in production.

• Strong analytical, problem-solving, and software development skills.

• Strong communication skills with the ability to explain technical concepts and findings effectively.

• Strong sense of ownership, accountability, and engineering rigor.

• Passionate about people leadership, with a drive toward ongoing learning and development in this area.

Preferred Qualifications

• Experience with semiconductor manufacturing, test, or yield data, including wafer probe and final test electrical data, inline metrology, defect inspection, fault-detection and equipment trace data, assembly and packaging data, or tool and chamber history.

• Direct experience in yield prediction or yield improvement modeling, die or wafer disposition, binning and screening rule development, DPM or reliability prediction, test-time or test-cost reduction, or excursion detection.

• Experience with memory products, advanced packaging, 3D stacking, or heterogeneous integration.

• Deep understanding of semiconductor-specific AI/ML applications.

• Experience using enterprise data platforms such as BigQuery, Snowflake, MSSQL, Oracle, or Redshift.

• MLOps tooling and practice, including experiment tracking, feature stores, model registries, pipeline orchestration, and containerized deployment.

• Experience with Kubernetes or similar production infrastructure and deployment frameworks.

• Experience with anomaly detection, survival analysis, causal inference, uncertainty quantification, or Bayesian methods.

• Experience designing scalable, enterprise-grade AI/ML systems with attention to reliability, traceability, reproducibility, and operational readiness.

• Experience building agentic systems or AI solutions for semiconductor manufacturing, product engineering, validation, yield improvement, reliability, or failure analysis.

• Familiarity with agentic AI frameworks such as LangGraph, Google ADK, or AutoGen, evaluation tools such as AgentEval, and modern AI coding tools such as Claude Code, Cursor, Cline, Windsurf, or Gemini CLI.

• Experience working in cross-functional environments spanning engineering, manufacturing, data science, and IT