Vision Engineer

The Vision Engineer is responsible for designing, developing, deploying, and sustaining advanced machine vision and automated inspection systems used in Cel Link’s manufacturing processes. This position combines expertise in industrial machine vision, computer vision, artificial intelligence/machine learning, and software development to create scalable inspection and process-monitoring solutions that improve product quality, yield, equipment performance, and manufacturing efficiency.

The Vision Engineer develops both traditional rule-based and AI-enabled vision applications, including the software, algorithms, data pipelines, interfaces, and system integrations required to deploy these solutions in a high-volume manufacturing environment.

• Design, develop, deploy, and optimize machine vision and computer vision systems, including cameras, optics, lighting, image acquisition, calibration, image-processing algorithms, inspection logic, measurement, defect detection, classification, and automated pass/fail decisions.
• Develop production software and applications supporting vision and inspection systems using languages and technologies such as Python, C#, C++, SQL, APIs, databases, and related development frameworks; maintain robust source control, testing, documentation, deployment, and software revision practices.
• Develop and deploy AI/ML-based computer vision solutions for defect detection, anomaly detection, classification, measurement, and process monitoring, including data preparation, model training, validation, deployment, performance monitoring, and continuous improvement.
• Integrate vision systems and software with manufacturing equipment and factory systems, including PLCs, motion systems, MES, databases, SPC, traceability systems, and other production data platforms to enable automated decisions, data collection, analytics, and closed-loop process improvement.
• Own vision system performance throughout the equipment lifecycle, including requirements development, vendor technical reviews, FAT/SAT, commissioning, qualification, troubleshooting, root-cause analysis, false reject/false accept reduction, documentation, training, and sustained production support.

• BS in Electrical Engineering, Computer Engineering, Computer Science, Mechanical Engineering, Mechatronics, Robotics, Optical Engineering, or a related technical field.
• 3+ years of hands-on experience developing, integrating, or supporting industrial machine vision, automated optical inspection, computer vision, or image-processing systems in a manufacturing or automation environment.
• Hands-on experience with industrial cameras, lenses, lighting, image acquisition, calibration, and image-processing techniques.
• Programming experience using Python, C#, C++, or similar languages for machine vision, automation, or manufacturing applications.
• Experience troubleshooting automated equipment and integrating vision systems with industrial controls, PLCs, or production equipment.

• Experience developing production software using Python, C#, C++, SQL, or similar technologies.
• Experience with software architecture, APIs, databases, source/version control, automated testing, and deployment practices.
• Experience with computer vision frameworks and libraries such as OpenCV, PyTorch, Tensor Flow, or equivalent technologies.
• Experience developing, training, validating, and deploying AI/ML models for industrial computer vision applications.
• Experience with industrial vision platforms such as Cognex, Keyence, Basler, Teledyne, Omron, or similar systems.
• Experience integrating software and vision applications with PLCs, MES, databases, SPC, traceability, and manufacturing data systems.
• Experience developing software and vision solutions for high-volume automated manufacturing equipment.
• Experience with statistical methods including GR&R, measurement-system analysis, process capability, and validation of automated inspection systems.

• Strong knowledge of industrial machine vision principles, including cameras, sensors, lenses, field of view, depth of field, resolution, exposure, triggering, lighting, optics, and image quality.
• Strong knowledge of image-processing techniques including filtering, thresholding, edge detection, feature extraction, pattern matching, measurement, segmentation, and defect classification.
• Knowledge of AI/ML computer vision techniques, including model development, training, validation, deployment, and…