XPENG
Senior Computer Vision Engineer - vLLM
XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity. We are seeking a passionate and skilled Computer Vision Engineer to design and implement cutting-edge computer vision systems. Your work will focus on in-cabin scenarios, including, but not limited to: detection and classification of occupants and objects, understanding and analyzing human actions and behaviors, and perceiving in-cabin ambience. Key Responsibilities
Design multimodal (image/video/text) models using state-of-the-art machine learning and neural network algorithms. Create and curate training datasets; iteratively refine data based on model performance and project requirements. Implement and optimize distributed training frameworks to accelerate model development. Collaborate with deployment teams to enable efficient edge and on-device model deployment. Stay current with academic research and integrate novel algorithms into production workflows. Coordinate cross-functional efforts across teams and departments. Minimum Requirements
Master's or Ph.D. in Computer Science or a related field, with strong expertise in computer vision and machine learning. Open to new graduates. Hands-on experience in model development (CV/VL models) and algorithm optimization. Proficiency in PyTorch or TensorFlow, and experience with data preprocessing techniques. Strong collaboration and communication skills. Ability to read, interpret, and implement research papers effectively. Preferred Qualifications
Experience coordinating across multiple teams. Familiarity with large-scale model pretraining, quantization, or distributed training. Publications in top-tier conferences (e.g., CVPR, ICCV, NeurIPS) or contributions to open-source projects. Experience with edge device deployment or applications in autonomous driving.
XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity. We are seeking a passionate and skilled Computer Vision Engineer to design and implement cutting-edge computer vision systems. Your work will focus on in-cabin scenarios, including, but not limited to: detection and classification of occupants and objects, understanding and analyzing human actions and behaviors, and perceiving in-cabin ambience. Key Responsibilities
Design multimodal (image/video/text) models using state-of-the-art machine learning and neural network algorithms. Create and curate training datasets; iteratively refine data based on model performance and project requirements. Implement and optimize distributed training frameworks to accelerate model development. Collaborate with deployment teams to enable efficient edge and on-device model deployment. Stay current with academic research and integrate novel algorithms into production workflows. Coordinate cross-functional efforts across teams and departments. Minimum Requirements
Master's or Ph.D. in Computer Science or a related field, with strong expertise in computer vision and machine learning. Open to new graduates. Hands-on experience in model development (CV/VL models) and algorithm optimization. Proficiency in PyTorch or TensorFlow, and experience with data preprocessing techniques. Strong collaboration and communication skills. Ability to read, interpret, and implement research papers effectively. Preferred Qualifications
Experience coordinating across multiple teams. Familiarity with large-scale model pretraining, quantization, or distributed training. Publications in top-tier conferences (e.g., CVPR, ICCV, NeurIPS) or contributions to open-source projects. Experience with edge device deployment or applications in autonomous driving.