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Jobfoundry
A*STAR

Senior Research Engineer I, FAB Operations, IME

A*STAR
ResearchSingaporeFull-time1 weeks ago
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About the role

AI summarised

Senior Research Engineer I role at IME's FAB Operations, focusing on developing and deploying data-driven solutions using machine learning, statistical modeling, and AI to improve semiconductor manufacturing outcomes. The role involves building data pipelines, predictive models, and collaborating with cross-functional teams to enhance productivity, quality, and yield.

ResearchFull-timeInstitute of Microelectronics

Key Responsibilities

  • Design, develop, and implement scalable data pipelines to ingest, clean, and structure high-volume, high-velocity data from fab tools (e.g., sensors, MES, EDA, APC systems).
  • Apply advanced analytics, machine learning, and AI techniques (e.g., computer vision, time-series forecasting, anomaly detection, reinforcement learning) to improve manufacturing outcomes.
  • Build predictive and prescriptive models for applications such as yield prediction and root cause analysis, equipment health monitoring and predictive maintenance, real-time process control and fault detection, defect classification and pattern recognition.
  • Collaborate with cross-functional teams to translate business problems into analytical frameworks and measurable KPIs.
  • Deploy and monitor ML models in production environments, ensuring reliability, scalability, and compliance with fab data governance standards.
  • Stay current with emerging AI/ML technologies and assess their applicability to semiconductor manufacturing challenges.
  • Document methodologies, share insights through dashboards/reports, and support continuous improvement initiatives.

Requirements

  • Minimal Bachelor in Data Science, Computer Science, Electrical Engineering, Industrial Engineering, Applied Mathematics, or a related field preferred.
  • 2+ years of experience applying data analytics and/or machine learning in semiconductor manufacturing or fab automation.
  • Knowledge of semiconductor processes (e.g., lithography, etch, deposition) or equipment data standards (SECS/GEM, GEM300).
  • Strong programming skills in Python (pandas, scikit-learn, TensorFlow/PyTorch) and SQL.
  • Experience working with time-series data, sensor data, or structured/unstructured manufacturing data.
  • Familiarity with cloud platforms (AWS, Azure, or GCP) and big data tools (e.g., Spark, Kafka).
  • Understanding of statistical methods, experimental design, and model validation techniques.
  • Excellent problem-solving, communication, and teamwork skills.