XPeng Motors is one of China’s leading smart electric vehicle (“EV”) companies. We design, develop, manufacture and sell smart EVs that are seamlessly integrated with advanced Internet, AI and autonomous driving technologies. We are committed to in-house R&D and intelligent manufacturing to create a better mobility experience for our customers. We strive to transform smart electric vehicles with technology and data, shaping the mobility experience of the future.
We are looking for motivated MS/PhD students as full-time interns to research and develop novel CV/ML/DL algorithms that possess potential for autonomous driving/smart cabin applications. You will be working with a team of computer vision and AI engineers to develop, implement and validate algorithms over public and private datasets, targeting conference/journal publications and patent filings.
Research and develop novel CV/ML/DL algorithms for autonomous driving/cabin intelligence
Implement and validate developed algorithms over public and private datasets
Publish in top-tier conferences/journals and file patents
MS/PhD students in Computer Science, Electrical Engineering, or related disciplines
Track-record R&D experience in one of the computer vision topics: object detection/segmentation/tracking, face recognition, 3D reconstruction, action recognition, etc.
Excellent programming skills and knowledge of C++ or Python
Familiar with OpenCV, Numpy and any deep learning frameworks: Pytorch, Tensorflow, etc.
Ability to follow SOTA advancements in AI and come up with new algorithm ideas
Excellent written and oral communication skills
PhD candidate students who have passed their prelim/proposal exam
Experience in one of the following topics: face detection, face recognition, pose estimation, 3D reconstruction, action recognition (driver monitoring)
Opportunities to pursue and work on cutting edge technologies
Snacks, lunches and fun activities
We are 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 prescribed category set forth in federal or state regulations.