Full-Time Machine Learning Engineer

• 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.

• 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.

• 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.

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