Computer Vision Algorithm Researcher

Description and Requirements Position Overview •Responsible for the research, optimization, and deployment of core algorithms in the field of computer vision, with a focus on technological breakthroughs in pattern recognition and deep learning. •Integrate technologies such as 3D vision and multimodal large-scale models to support innovation and real-world implementation across business scenarios. •Develop efficient and robust algorithmic solutions, driving the translation of technical research into practical outcomes. Responsibilities •Deeply engage in pattern recognition, computer vision, traditional machine learning, and modern deep learning; take ownership of the design, development, debugging, and iterative optimization of core algorithms, ensuring accuracy, efficiency, and robustness. •Lead or participate in projects involving classifier design, semantic segmentation, 3D vision, measurement operator development, and robustness analysis, driving the full lifecycle from algorithm design and model training to production deployment. •Explore the application of generative models, multimodal large-scale models, and agent technologies in computer vision scenarios; conduct technical pre-research and innovative practices to solve complex real-world business challenges. •Build algorithm development, testing, and validation environments using tools such as Python, OpenCV, and PyTorch; complete algorithm prototyping, performance evaluation, and iterative improvement. •Track the latest research from top-tier conferences including ICCV, CVPR, ICML, ICLR, and NeurIPS; translate cutting-edge research into business applications and continuously enhance the team’s technical capabilities. Qualifications •Education: PhD degree in Computer Science, Automation, Electronic Engineering, Artificial Intelligence, or a related field, with a solid theoretical foundation. •Programming Skills: Expert-level proficiency in Python; familiarity with Linux development, debugging, and deployment; strong hands-on experience with OpenCV and PyTorch for model construction, training, and optimization. •Algorithm Expertise: Strong background in pattern recognition, computer vision, traditional machine learning, and modern deep learning. •Project Experience: At least 5 years of hands-on experience applying classifier design, semantic segmentation, 3D vision, measurement operators, robustness analysis, generative models, and multimodal large models in real-world projects. •Research Achievements: Publications at top-tier conferences such as ICCV, CVPR, ICML, ICLR, or NeurIPS; first-author or corresponding-author papers are preferred.