AI/ML Analytics Engineer (2027 New College Graduate)

GlobalFoundries (GF) is a leading manufacturer of essential semiconductors, enabling AI at scale from the cloud to the physical world. Through deep partnerships with customers, GF delivers differentiated, power‑efficient and high‑performance solutions for high‑growth markets. GF is a trusted and holistic technology partner for customers around the world. GF’s talented, global team remains focused every day on security, longevity and sustainability. For more information, visit www.gf.com.

We offer many full-time employment paths for recent graduates, which provide accelerated training in a fast-paced work environment, cross-functional working opportunities, and talent mobility. New college graduates are provided with mentorship, networking, and leadership opportunities, which give our new team members life-long connections and skills.

About the role

GlobalFoundries is seeking a new college graduate to join the Analytics Center of Excellence, Digital Manufacturing and help develop data-driven solutions for semiconductor manufacturing. Working closely with the Analytics Center of Excellence Digital Manufacturing Manager and cross-functional Fab teams, this engineer will define project objectives, prepare manufacturing datasets, apply statistical and machine learning methods, develop predictive agentic solutions, and communicate actionable recommendations to stakeholders and Fab leadership.

Responsibilities

• Partner with the Analytics Center of Excellence Digital Manufacturing Manager to define project objectives, deliverables, success measures, and timelines; support the design, development, programming, testing, and documentation of analytics methods, processes, and systems across multiple use cases.
• Collaborate with manufacturing, engineering, and other cross-functional teams to understand business needs, identify opportunities for artificial intelligence, machine learning, and digital manufacturing solutions, and translate requirements into measurable data-driven approaches.
• Collect, clean, integrate, and preprocess large datasets from multiple sources; apply statistical analysis, visualization, and machine learning techniques to identify patterns, trends, root causes, and