2027 Engineering Intern - AI/ML & Tool Innovation (SABRE Electroplating) - Masters/PhD (6 months) is a role within the SABRE Process Development team focused on shaping next-generation semiconductor electroplating equipment. The intern will apply engineering principles, data analytics, machine learning, and automation to enhance tool performance, process understanding, and intelligent equipment capabilities. Daily responsibilities include working on projects involving tool design concepts, experimental data analysis, predictive modeling, digital twins, machine vision, sensor integration, and AI-driven process optimization. Candidates should possess strong engineering fundamentals and experience with Python, data analytics, machine learning, or computer vision. The role requires the ability to work across hardware, software, and process disciplines to solve complex problems in advanced manufacturing systems. This position specifically addresses technical challenges in semiconductor equipment, automation, control systems, and digital twin integration for electroplating processes.
Apply engineering principles to improve semiconductor electroplating equipment performance.
Analyze experimental data to enhance process understanding and tool capabilities.
Develop predictive models and digital twins for manufacturing systems.
Implement machine vision and sensor integration into production tools.
Utilize machine learning and AI to optimize manufacturing processes.
Design new concepts for semiconductor tool hardware and software.
Automate equipment functions to improve overall operational efficiency.
What we're looking for
Pursuing MS or PhD in Mechanical Engineering, Electrical Engineering, Chemical Engineering, Computer Science, Data Science, Robotics, or a related field (preferred).
Strong engineering fundamentals and problem-solving skills (preferred).
Experience with Python, data analytics, machine learning, or computer vision (preferred).
Interest in semiconductor equipment, automation, control systems, or digital twins (preferred).
Ability to work across hardware, software, and process disciplines (preferred).
Silicon Design
High-speed I/O
Python
Perl
Unix
Linux
Windows
Firmware
Oscilloscopes
Signal Generators
BERT
Logic Analyzers
Training Algorithms
Equalization
Automation
Data Science
Machine Learning
High Performance Computing
Quantum Computing
Hybrid Cloud
Chip Architecture
Semiconductor Research
Data Analysis
Information Technology