XPENG & Volkswagen Group
Staff Computer Vision Engineer - vLLM
XPENG & Volkswagen Group, Santa Clara, California, us, 95053
XPENG
– Computer Vision Engineer
XPENG is a leading smart technology company focused on AI, autonomous driving, and intelligent mobility across EVs, eVTOL aircraft, and robotics. We are seeking a passionate and skilled
Computer Vision
Engineer
to design and implement in-cabin computer vision systems, including detection and classification of occupants and objects, understanding 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. Master’s or Ph.D. in Computer Science or a related field, with strong expertise in computer vision and machine learning. 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. Compensation : The base salary range for this full-time position is $215,280-$364,320 in addition to bonus, equity and benefits. Salary ranges are determined by role, level, and location. The range displayed reflects the minimum and maximum target for new hire salaries across all US locations. Within the range, pay is determined by work location and factors such as skills, experience, and education. Equal Opportunity Employer : XPENG is an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other category protected by law. Voluntary Self-Identification
For government reporting purposes, we invite candidates to respond to the voluntary self-identification survey. Completion is voluntary and will not affect hiring decisions. Any information provided is kept confidential. More details are available in the form instructions and applicable regulations. Public burden statement: This survey is conducted under applicable law and may include an Office of Federal Contract Compliance Programs (OFCCP) requirement.
#J-18808-Ljbffr
– Computer Vision Engineer
XPENG is a leading smart technology company focused on AI, autonomous driving, and intelligent mobility across EVs, eVTOL aircraft, and robotics. We are seeking a passionate and skilled
Computer Vision
Engineer
to design and implement in-cabin computer vision systems, including detection and classification of occupants and objects, understanding 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. Master’s or Ph.D. in Computer Science or a related field, with strong expertise in computer vision and machine learning. 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. Compensation : The base salary range for this full-time position is $215,280-$364,320 in addition to bonus, equity and benefits. Salary ranges are determined by role, level, and location. The range displayed reflects the minimum and maximum target for new hire salaries across all US locations. Within the range, pay is determined by work location and factors such as skills, experience, and education. Equal Opportunity Employer : XPENG is an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other category protected by law. Voluntary Self-Identification
For government reporting purposes, we invite candidates to respond to the voluntary self-identification survey. Completion is voluntary and will not affect hiring decisions. Any information provided is kept confidential. More details are available in the form instructions and applicable regulations. Public burden statement: This survey is conducted under applicable law and may include an Office of Federal Contract Compliance Programs (OFCCP) requirement.
#J-18808-Ljbffr