Position Overview

•Lead the research, optimization, and deployment of core algorithms in computer vision, with a focus on technological breakthroughs in pattern recognition and deep learning.
•Integrate technologies such as 3D vision and multimodal large models to support innovation and real-world implementation in business scenarios.
•Develop efficient and robust algorithmic solutions, driving the transformation of technical research into practical outcomes.

Responsibilities

•Lead projects related to classifier design, semantic segmentation, 3D vision, measurement operator development, and robustness analysis, overseeing the full lifecycle from algorithm design and model training to engineering deployment.
•Explore the application of generative models and multimodal large models 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 optimization.
•Track the latest research from top-tier conferences such as ICCV, CVPR, ICML, ICLR, and NeurIPS; translate cutting-edge research into business-oriented solutions and drive continuous improvement of the team’s technical capabilities.

Qualifications

•Education: PhD or Master’s 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 building, training, and optimization.
•Algorithm Expertise: Strong background in pattern recognition, computer vision, traditional machine learning, modern deep learning, multimodal large models, and industry agent technologies.
•Project Experience: At least 3 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 in machine learning or computer vision conferences; first-author or corresponding-author papers are preferred.