Process AI Automation, Intern

We are seeking a motivated engineering intern to support the implementation of Smart Visual Inspection within our Austin Order Fulfillment and packaging operations. The intern will work with engineering, operations, quality, and production teams to develop and validate a computer-vision-enabled inspection process for packaging and finished-product verification.

The internship will provide hands-on experience with smart manufacturing, computer vision, machine learning, manufacturing process improvement, inspection-system development, and operational deployment. The intern will help convert existing inspection requirements into repeatable digital workflows, develop image datasets, configure inspection recipes, validate model performance, optimize camera and lighting conditions, and document the solution for production use.

The Smart Visual Inspection platform enables engineers to create guided inspection procedures, train and deploy AI models, provide immediate pass/fail feedback, and capture inspection results for traceability. The Austin proof of concept will initially focus on pack-out applications such as verifying product accessories, quantities, text, labels, orientation, and packaging contents

• Assist with the development and implementation of Smart Visual Inspection solutions for Order Fulfillment, packaging, and finished-product inspection.
• Study current inspection and pack-out processes, identify improvement opportunities, and translate operational requirements into structured inspection workflows.
• Work with engineers and production personnel to identify representative products and packaging configurations for proof-of-concept testing.
• Capture, organize, and maintain image datasets under representative operating conditions.
• Label and annotate images used for computer-vision model training.
• Safely move, position, and prepare product cartons and packaging materials while capturing images from multiple angles for computer-vision model development and validation.
• Develop and maintain digital inspection procedures and inspection recipes.
• Train and test computer-vision models for applications such as:
  • Object presence and absence verification
  • Quantity verification
  • Packaging-content validation
  • Label and text recognition
  • Product-position and orientation checks
  • Region-based classification
• Evaluate model performance and help adjust confidence thresholds to balance false accepts and false rejects.
• Conduct controlled tests using representative products, packaging materials, accessories, labels, and configurations.
• Support the setup and optimization of cameras, fixtures, workstations, backgrounds, and diffuse lighting.
• Evaluate how camera position, lighting, glare, shadows, object placement, and image quality affect inspection reliability.
• Record test results, model performance, inspection exceptions, process times, and identified improvement opportunities.
• Assist with root-cause analysis and troubleshooting when an inspection process produces inconsistent or unexpected results.
• Develop standard operating procedures, work instructions, training materials, and technical documentation.
• Support operator training and collect feedback during pilot deployment.
• Collaborate with cross-functional teams to incorporate operational feedback and improve the inspection process.
• Help establish a repeatable process for maintaining inspection procedures, trained models, configuration files, and revision history.
• Present project status, technical findings, validation results, and final recommendations to engineering and operations stakeholders.

These responsibilities reflect the project's documented needs around image capture and annotation, model training, validation thresholds, camera and lighting consistency, inspection-workflow development, traceability, and pack-out verification

• Currently pursuing a bachelor's or master's degree in:
  • Industrial Engineering
  • Manufacturing Engineering
  • Computer Engineering
  • Electrical Engineering
  • Mechanical Engineering
  • Computer Science
  • Data Science
  • Robotics, Automation, or a related technical discipline
• Basic understanding of manufacturing, engineering, quality, automation, computer vision, or data-analysis concepts.
• Strong analytical and structured problem-solving skills.
• Ability to collect, organize, analyze, and clearly present technical data.
• Ability to follow defined test methods and accurately document results.
• Ability to work effectively in a hands-on manufacturing or operations environment.
• Strong written and verbal communication skills.
• Ability to collaborate with engineers, operators, quality personnel, and other cross-functional stakeholders.
• Proficiency with Microsoft Excel, PowerPoint, and Word.
• Willingness to learn new software tools, inspection methods, and manufacturing processes.
• Ability to manage assigned tasks with appropriate guidance and communicate progress, risks, and technical issues.
• Ability to occasionally lift and move boxes weighing up to 50 pounds, with or without reasonable accommodation, as needed to position products and packaging materials for image capture, model training, and inspection testing.

• Coursework or project experience in one or more of the following:
  • Computer vision
  • Machine learning
  • Image processing
  • Industrial automation
  • Robotics
  • Manufacturing systems
  • Quality engineering
  • Operations research
  • Lean manufacturing or Six Sigma
  • Data analytics
• Experience using Python, MATLAB, OpenCV, or another analytical or image-processing tool.
• Experience collecting, cleaning, labeling, or analyzing image datasets.
• Familiarity with object detection, optical character recognition, classification, or anomaly-detection concepts.
• Experience with cameras, lighting, sensors, fixtures, or automated inspection equipment.
• Familiarity with process mapping, time studies, root-cause analysis, validation, or statistical analysis.
• Experience creating standard operating procedures, work instructions, test reports, or technical presentations.
• Interest in smart manufacturing, Industry 4.0, digital work instructions, artificial intelligence, and operations technology.
• Previous internship, laboratory, capstone, research, or project experience in a manufacturing or engineering environment.