Senior Equipment & Factory Control Automation Engineer

About our group

Equipment Engineering is the backbone of Seagate's wafer manufacturing excellence. Our team ensures every piece of tooling operates at peak performance to fabricate the Magnetic Recording Heads that power Seagate's world‑class HDDs.

We combine deep equipment engineering expertise with advanced factory control systems, sensors, and AI‑driven technologies to monitor tool health in real time and enable proactive, automated decision‑making.

Join Seagate's Wafer AI vision, where we are transforming traditional manufacturing into a smart factory. You'll work on high‑impact initiatives with measurable ROI, collaborating with global experts across engineering, AI, and operations in a culture that values innovation, continuous learning, and teamwork.

This role is ideal for someone who enjoys hands‑on equipment work while also building next‑generation automation, data systems, and smart factory capabilities.

• You will bridge traditional equipment engineering with intelligent automation, support, optimize, and modernize semiconductor manufacturing equipment while driving adoption of data‑driven control strategies and AI‑enabled solutions.
• Support development, deployment, and optimization of wafer processing equipment across areas such as Photolithography, Electromagnetic Plating, or Metrology.
• Lead installation, modification, upgrade, and maintenance of manufacturing equipment to improve performance and reliability.
• Evaluate equipment health and drive actions to improve uptime, throughput, and process stability.
• Develop and implement factory control strategies using automation, sensors, and AI‑driven monitoring solutions.
• Deploy data collection systems and integrate tools using software (e.g., Python, SQL, AI frameworks) to enhance tool performance and decision‑making.
• Build or support lightweight AI models and rule‑based logic for anomaly detection, predictive maintenance, and automated interlocks.
• Partner with Equipment, Process, Yield, and Data Engineering teams to solve complex manufacturing issues and identify high‑value improvement opportunities.
• Lead or support root cause investigations using engineering fundamentals, statistical analysis, and data insights.
• Maintain documentation on tool upgrades, safety issues, and technical notices from equipment suppliers.
• Provide technical support to technicians, operators, and engineering teams, and contribute to knowledge sharing across the organization.

• Strong problem‑solving mindset with the ability to translate complex issues into practical solutions.
• Excellent communication skills and ability to influence cross‑functional teams.
• Comfortable working in a fast‑paced manufacturing environment, including occasional off‑shift support as needed.
• Passion for improving manufacturing through automation, data, and intelligent systems.
• Collaborative mindset with a willingness to both teach and learn within the team.

• Demonstrated experience in semiconductor or wafer manufacturing equipment.
• Hands‑on experience with equipment systems in areas such as Photolithography, Plating, Metrology, or Inspection.
• Understanding of equipment performance, reliability, and maintenance practices.
• Experience with statistical analysis tools (e.g., JMP, Minitab, Six Sigma methodologies).
• Proficiency in programming or scripting (e.g., Python, SQL, R, or similar).
• Experience or exposure to implementing equipment control strategies or factory automation systems.
• Bachelor's degree in engineering related field (i.e., Electrical, Mechanical, Chemical, Computer Engineering, Computer Science, AI/ML, Materials, Physics, or Information Technology) and 5+ years of experience or master's degree in the same and 3+ years' experience or PhD and 0+ years' experience or equivalent experience and education.

• Experience integrating sensors, edge devices, or data acquisition systems into manufacturing equipment.
• Familiarity with AI/ML concepts applied to equipment monitoring or fault detection.
• Experience with containerization or ML deployment tools (e.g., Docker, Kubernetes, MLflow).
• Background in upgrading legacy tools with modern…