At Bot Auto, the business problem is to develop cutting-edge autonomous technologies that directly impact how self-driving cars perceive and navigate the world, specifically in well-maintained road environments and challenging construction zones.
Requirements
- 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++.
- 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.
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.
Other
- Have an advanced degree (Ph.D or Master’s) in related fields of study: computer science, computer engineering, robotics, mathematics, and etc.
- Strong problem-solving skills and ability to work in a fast-paced, research-driven environment.
- Collaborate with experts across AI, mapping, and robotics
- Work in a fast-paced, research-driven environment
- None
