Principal/Staff ML/AI Engineer, Product Engineering, STPG (Evergreen)

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD.; Singapore; On-site; SGD 120,000 - 210,000; Full time

Job summary

Micron Semiconductor Asia Operations Pte. Ltd. is seeking an experienced AI/ML Engineer to develop and deploy predictive analytics and agentic AI solutions for semiconductor manufacturing workflows.

You will mentor Citizen Data Scientists, build scalable ML pipelines, and collaborate with cross-functional teams to drive data‑driven decisions that improve yield, quality and cycle times.

Qualifications

• PhD with ≥2 years, or Master’s with ≥5 years, or Bachelor’s with ≥7 years in CS/DS/AI.
• ≥6 years of experience in ML, data science or AI solution development.
• Production‑level ML solutions experience and large datasets.
• Ability to lead technical initiatives across multidisciplinary teams.

Responsibilities

• Develop, validate, and deploy ML models for yield, test time, reliability, and process improvement.
• Apply analytics to large-scale semiconductor data and build scalable ML workflows.
• Design AI agents to automate engineering and operations workflows.
• Partner with engineering to acquire, cleanse, transform data for AI apps.
• Mentor Citizen Data Scientists with training materials and sessions.
• Stay current with AI, ML, MLOps and semiconductor tech trends.

Skills

Python

Pandas

NumPy

Scikit-learn

Supervised learning

Unsupervised learning

Regression models

Decision trees

Random forests

Gradient boosting

Predictive modeling

ML workflows

Feature engineering

Statistical analysis

Education

PhD or Master’s in CS/Data Science/AI

Bachelor’s in a relevant field

Job description

Job Summary:

Join the Product Engineering team to develop and deploy Artificial Intelligence (AI) and Machine Learning (ML) solutions that transform semiconductor manufacturing and engineering workflows. This role is responsible for building predictive analytics models, developing agentic AI solutions, enabling intelligent automation, and driving data-driven decision-making to improve yield, quality, cycle time, reliability, and cost efficiency.

The successful candidate will leverage advanced machine learning techniques, large-scale semiconductor datasets, and AI technologies to solve complex engineering challenges. In addition, this role will mentor and enable Citizen Data Scientists (CDS) by providing technical guidance, training, and best practices in machine learning, analytics, and AI adoption. Strong technical expertise, innovation, collaboration, and communication skills are essential for success.

Main Responsibilities:

Machine Learning Development

Develop, validate, and deploy machine learning models for yield prediction, test time optimization, reliability analysis, and manufacturing process improvement.

Apply advanced analytics and machine learning techniques to large-scale structured and semiconductor manufacturing datasets.

Build scalable ML workflows and predictive solutions that generate actionable engineering insights.

Evaluate model performance and continuously improve model accuracy and effectiveness.

Agentic AI Development

Design and develop intelligent AI agents to automate engineering and operational workflows.

Build AI-driven solutions for applications such as yield analysis, test program validation, root cause investigation, engineering report generation, and process optimization.

Integrate AI agents with enterprise platforms, collaboration tools, and business systems.

Data Engineering & Analytics

Partner with engineering teams to acquire, cleanse, transform, and structure high-volume manufacturing, test, probe, and inline process data.

Develop data pipelines and frameworks that support AI and machine learning applications.

Collaborate with deployment and architecture teams to ensure robust production implementation and scalable inferencing solutions.

Citizen Data Scientist (CDS) Enablement

Mentor and coach Citizen Data Scientists in applying machine learning methodologies and analytics best practices.

Develop training materials, learning paths, workshops, and hands-on technical sessions.

Review AI/ML projects and provide technical guidance to improve solution quality and business impact.

Foster a culture of data-driven decision-making and AI adoption across Product Engineering.

Other Responsibilities:

Collaborate with cross-functional engineering, manufacturing, data science, and IT teams to identify opportunities for AI-driven improvements.

Drive innovation initiatives that reduce test time, improve yield, enhance product quality, and automate manual processes.

Contribute to technical publications, patent disclosures, engineering standards, and internal best practices.

Stay current with emerging AI, machine learning, MLOps, and semiconductor industry technologies.

Support strategic AI initiatives and participate in technology roadmap discussions.

AI Responsibilities:

Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements.

Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one's scope of work.

Minimum Required Qualifications/Experience

PhD with ≥2 years, or Master's degree with ≥5 years, or Bachelor's degree with ≥7 years of relevant experience in Computer Science, Data Science, Electrical/Electronic Engineering, Artificial Intelligence, or a related field

Minimum of 6 years of relevant industry experience in machine learning, data science, or AI solution development.

Proven experience developing and implementing machine learning solutions in production environments.

Experience working with large-scale structured datasets and predictive analytics applications.

Demonstrated ability to lead technical initiatives and collaborate effectively across multidisciplinary teams.

Must Have Technical Skills

• Strong programming skills in Python.
• Proficiency with data analytics and machine learning libraries such as Pandas, NumPy, Scikit-learn, and related tools.
• Strong understanding of supervised and unsupervised machine learning techniques.
• Experience with regression models, decision trees, random forests, gradient boosting, and predictive modeling.
• Ability to develop, validate, and optimize machine learning models for business and engineering applications.
• Experience handling large-scale datasets and building end-to-end ML workflows.
• Understanding of statistical analysis, feature engineering, model evaluat