Principal AI Engineer - HBM Yield & Analytics (Singapore)

Micron Technology, Inc.; Singapore; Hybrid; SGD 180,000 - 240,000; Full time

Job summary

Micron Technology, Inc. in Singapore seeks a Principal Engineer to lead the Heterogeneous Integration Group’s advanced data analytics for HBM product engineering.

You will mentor engineers and drive predictive models that improve yield, quality, and cycle time. This role focuses on building scalable data pipelines, deploying ML in production, and collaborating with Product, Test, Yield, Process, and Fab teams.

Qualifications

• Minimum 5 years of hands-on machine learning, data science, or predictive modeling experience.
• 2+ years leading a team of engineers or data scientists as a people manager or formal technical lead.
• Demonstrated depth in gradient-boosted tree methods (e.g., XGBoost, LightGBM, CatBoost).
• Experience with time-series forecasting, drift detection, and rolling-window features.
• Production ML deployment experience with monitoring and retraining triggers.
• Strong Python and SQL programming and data analytics foundations.

Responsibilities

• Mentor and develop team members to foster growth within the team.
• Lead modeling work, set technical direction, and balance workload.
• Develop and productionize ML models for classification, regression, anomaly detection, and failure analysis.
• Build scalable data pipelines and analytical workflows for large, heterogeneous datasets.
• Apply GenAI and agentic augmentation to streamline engineering workflows.
• Collaborate with Product, Test, Yield, Process, and Fab teams and communicate results clearly.

Skills

Python

SQL

Data analytics

Machine learning

Leadership

XGBoost

Time-series

MLOps

Education

Bachelor/Master in a relevant field

Tools

BigQuery

Snowflake

Kubernetes

Job description

Micron Technology, Inc. in Singapore seeks a Principal Engineer to lead the Heterogeneous Integration Group’s advanced data analytics for HBM product engineering.

You will mentor engineers and drive predictive models that improve yield, quality, and cycle time. This role focuses on building scalable data pipelines, deploying ML in production, and collaborating with Product, Test, Yield, Process, and Fab teams.