Machine Learning Engineer

Why Join Us At Bot Auto, you’ll work on cutting-edge autonomous technologies that directly impact how self-driving cars perceive and navigate the world. You’ll collaborate with experts across AI, mapping, and robotics, shaping the next generation of intelligent mapping systems. About the Role We are seeking a highly motivated Machine Learning Engineer to join our HD mapping team. The ideal candidate will develop learning-based algorithms for online map building in both well-maintained road environments and challenging construction zones. You will leverage cutting-edge deep learning and transformer-based architectures to improve our real-time mapping and perception systems, which serve as the foundation for safe and scalable autonomous driving. Key Responsibilities • Design, train, and deploy deep learning models for lane marking and road feature detection using camera, LiDAR, and other sensor data. • Develop transformer-based architectures and leverage other modern deep learning techniques for spatial-temporal perception and HD map updating. • Handle complex scenarios such as poorly painted lanes and temporary construction areas in dynamic weather conditions. • Collaborate with perception, localization, and planning teams to integrate learning-based map components into the autonomous driving system. • Conduct data analysis, dataset curation, and annotation for model training and evaluation. Qualifications Required • Have an advanced degree (Ph.D or Master’s) in related fields of study: computer science, computer engineering, robotics, mathematics, and etc.  • In-depth knowledge and extensive experience in deep learning, computer vision, and modern transformer architectures. • Hands-on experience with ML frameworks such as PyTorch or TensorFlow. • Solid programming skills in Python and preferably C++. • Strong problem-solving skills and ability to work in a fast-paced, research-driven environment. Preferred • Have a proven track record of research publications in top machine learning conferences and/or journals. • Prior experience in autonomous driving perception, semantic segmentation, online map generation, or multi-modal sensor fusion is highly desirable. • Experience with real-world deployment of perception models in robotics or autonomous systems. • Background in handling large-scale datasets and real-time processing pipelines.