Machine Learning Intern: Computer Vision for Manufacturing

ABB; Cary (NC); On-site; USD 27,552 - 46,838; Full time

Job summary

ABB Information Systems – Machine Learning Intern in Cary, NC is a hands-on internship designed to expose you to real-world projects within a global manufacturing environment.

You will work with factory specialists to formalize a business case, design and implement a computer vision system to capture parts images, develop ML models for counting and defect detection, and integrate the solution with existing manufacturing systems; strong teamwork and time management are essential.

Qualifications

• Pursuing a bachelor's degree in CS/CE or a related field in the United States.
• Strong analytical and problem-solving abilities.
• Ability to prioritize tasks and manage time effectively.
• Willingness to collaborate with cross-functional teams.

Responsibilities

• Work closely with factory specialists to formalize the business case.
• Design and implement a computer vision system to capture images of parts on a conveyor.
• Select the technical elements to build the solution.
• Develop machine learning models for part counting and defect detection.
• Work with image processing techniques to ensure high-quality image analysis.
• Integrate the solution with existing manufacturing systems.
• Test and validate the solution in real manufacturing environments.
• Document development processes and provide support for troubleshooting.
• Propose a plan to scale up the solution.

Skills

Critical thinking

Time management

Team collaboration

Task prioritization

Problem solving

Education

Bachelor's degree in Computer Science / Computer Engineering (US)

Job description

ABB Information Systems – Machine Learning Intern in Cary, NC is a hands-on internship designed to expose you to real-world projects within a global manufacturing environment.

You will work with factory specialists to formalize a business case, design and implement a computer vision system to capture parts images, develop ML models for counting and defect detection, and integrate the solution with existing manufacturing systems; strong teamwork and time management are essential.