Staff AI Data Engineer – Wafer Fabrication

Coherent•Fremont, CA

13h•Hybrid

About The Position

Analyze datasets generated by wafer fabrication processes, equipment, sensors, metrology systems, and manufacturing execution systems. Support multiple projects and drive forward detailed data analysis employing various ML/AI tools. Apply statistical analysis, AI, machine learning, and data-mining techniques for deeper understanding of devices and fabrication processes, process monitoring and anomaly detection, wafer and lot excursion analysis, yield analysis and prediction, and root-cause investigation. Collaborate with others using AI & ML. Integrate data from multiple sources, including process recipes, equipment logs, sensor data, metrology results, defect inspection, and production history. Translate analytical results into clear engineering insights and actionable recommendations. Communicate these findings to engineers and managers. Work with engineers to distinguish correlation from likely physical or process-driven causation. Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods. Support design of experiments, process characterization, and continuous improvement activities. Help establish best practices for data quality, feature engineering, model validation, documentation, and responsible use of AI in development and manufacturing.

Requirements

Minimum of 2 years of experience with AI, preferably including ML, preferably with experience in statistical analytic techniques.
Bachelor’s degree in Data Science, Engineering, Science, Computer Science or Mathematics is required.
Minimum two years of professional experience applying machine learning, artificial intelligence, and data science to real-world engineering or manufacturing problems.
Demonstrated ability to work simultaneously on multiple projects, in detail.
Demonstrated ability to work in teams involving members from Engineering, Manufacturing, MES Data Systems and AI/ML, with the ability to explain complex analytical results in practical engineering terms.
Experience with handling large data sets in agentic AI/ML.
Experience with SQL and modern data-processing or data-platform technologies.
Programming skills in Python and experience with common data-science and machine-learning libraries.
Experience in AI/ML model deployment through RESTful APIs, containerization, and container orchestration.
Experience working with structured, time-series, sensor, or high-volume manufacturing data.
Basic knowledge of statistical methods, experimental design, regression, classification, clustering, anomaly detection, and model evaluation.
Demonstrated ability to take small projects from problem definition through deployment, validation, and communication of results.

Nice To Haves

A Master’s degree is preferred.
Experience with cloud-based ML training and inference using AWS, GCP, Azure, or Databricks is a plus.
Exposure to wafer fabrication, photonics, and telecommunications is a plus.

Responsibilities

Analyze datasets generated by wafer fabrication processes, equipment, sensors, metrology systems, and manufacturing execution systems.
Support multiple projects and drive forward detailed data analysis employing various ML/AI tools.
Apply statistical analysis, AI, machine learning, and data-mining techniques for deeper understanding of devices and fabrication processes, process monitoring and anomaly detection, wafer and lot excursion analysis, yield analysis and prediction, and root-cause investigation.
Integrate data from multiple sources, including process recipes, equipment logs, sensor data, metrology results, defect inspection, and production history.
Translate analytical results into clear engineering insights and actionable recommendations.
Communicate these findings to engineers and managers.
Work with engineers to distinguish correlation from likely physical or process-driven causation.
Develop reusable data pipelines, analytical tools, dashboards, and model-monitoring methods.
Support design of experiments, process characterization, and continuous improvement activities.
Help establish best practices for data quality, feature engineering, model validation, documentation, and responsible use of AI in development and manufacturing.