To automate varied manufacturing processes and enhance operational efficiency and productivity through automated inspection, vision and AI related projects
Requirements
- Strong knowledge of OpenCV, PyTorch/TensorFlow, and image processing techniques
- Experience with deep learning models such as YOLOv5/8, Mask R-CNN, U-Net, or EfficientNet
- Familiarity with machine vision hardware: industrial cameras, lighting, lenses
- Edge deployment experience with Jetson Nano/Xavier, OpenVINO, or Coral TPU
- Proficient in Python; C++ or Rust is a bonus
- Basic knowledge of PLC interfacing and factory communication protocols
- Familiar with data annotation tools
- Experience with Git, Docker, and model versioning workflows
Responsibilities
- Design and implement real-time computer vision systems for defect detection in wood components at various production stages
- Select and calibrate imaging hardware tailored to each inspection task
- Develop, train, and deploy deep learning models
- Integrate vision solutions with factory automation systems
- Build image labeling pipelines and manage high-quality training datasets
- Monitor system performance, retrain models based on false positives/negatives, and maintain a continuous improvement loop
- Create dashboards and visualizations to monitor defect trends and production KPIs
Other
- B.Sc. or M.Sc. in Computer Science, Electrical Engineering, Robotics, or a related field
- 3+ years of hands-on experience in computer vision or industrial automation
- Excellent communication skills to interact effectively with plants, stakeholders, and cross-functional teams
- Ability to work independently and manage multiple projects simultaneously
- Project management skills to coordinate and oversee automation projects from inception to completion
