Job summary:
With the rapid evolution of manufacturing towards Industry 4.0, traditional rule-based AOI systems are increasingly challenged by complex products, high-mix production, and stringent quality requirements.
Our team is building next-generation AI-powered industrial AOI systems, focusing on high-precision visual AOI and edge-deployable AI solutions. The goal is to enable manufacturing equipment with robust visual perception, intelligent decision-making, and scalable deployment capability.
Key Technology Directions:
- Deep learning based industrial visual AOI inspection
- Edge AI deployment for manufacturing equipment (Embedded System / Industrial IPC)
- Intelligent AOI system architecture for smart manufacturing
Duties/ Responsibilities:
- Multimodal AOI Algorithm Development Design and implement deep neural networks for defect detection, visual alignment / positioning, irregular shape detection using visual cameras, structured-light, or X-ray data.
- Model Deployment & Performance Optimization Deploy trained models on edge devices and integrate into manufacturing lines. Optimize inference performance (latency, throughput, memory) for real production lines.
- Explore few-shot and zero-shot AOI concepts and participate in system architecture design and implementation using large language models.
- Involved in Smart Factory AI Agents System design and implementation.
Requirements:
- Master's degree or above in Computer Vision, Machine Learning, Automation, Industrial AI / AOI or related fields.
- Solid understanding of Solid understanding of computer vision and deep learning fundamentals.
- Practical experience with defect detection, segmentation, visual alignment / positioning, and common AOI algorithms.
- Hands-on experience with multimodal deep neural network models fine-tuning and inference optimization.
- Proficiency in Python and PyTorch, and Linux C/C++ programming.
- Preferred experience with:
Industrial cameras and lighting
NVIDIA TensorRT, CUDA, or edge AI optimization
ROS 2 or real-time system integration
Understanding of industrial communication protocols(EtherCAT, CAN), or PLC integration
Experience deploying AI models in real production lines
Experience with multimodal or vision-language models
Exposure to intelligent factory or AI agent concepts
