Senior Machine Learning Engineer, Computer Vision, HD Map and SLAM

Company Introduction At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality. Key Responsibilities Explore and propose new ideas using your knowledge and experience in deep learning , neural networks and large foundation models in autonomous driving including: end-to-end object detection, tracking and prediction, end-to-end planning and control, and end-to-end autonomous driving system, end-to-end online mapping. SLAM in localization and etc.. Work on the entire life cycle of machine learning projects from data analysis, model experimentations to performance metrics verifications, and understand the entire workflow in great detail. Be exposed to many cross-team projects and collaborate with the product, simulation and other sibling autonomous driving algorithm teams to extend machine learning technology to all components. Qualifications Required: Have an advanced degree (Ph.D or Master’s) in related fields of study: computer science, computer engineering, robotics, mathematics, physics, and etc. Have in-depth knowledge and extensive experience in machine learning, and/or computer vision, modern transformer architecture, and employ SOTA techniques of machine learning. Be familiar with PyTorch, TensorFlow and other machine learning platforms and tools. Have strong motivation to work independently in a fast paced environment while collaborating with other teams on more complex and larger projects. Preferred: Have a proven track record of research publications in top conferences and/or journals as the first author. Have knowledge and experience of generative models, model distillation, or model inference acceleration (e.g. TensorRT) techniques.

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