Description
The role is responsible for researching and developing end-to-end object detection and tracking models for autonomous driving, including pure-vision and mid-level-fusion approaches. It also involves exploring deep-learning methods for real-time perception, evaluating and optimizing in-vehicle machine-learning models, deploying them in a hardware-friendly manner, and resolving issues encountered during real-vehicle testing. The position requires a master's degree or higher in a relevant field, experience with end-to-end models, and proficiency with TensorFlow or PyTorch; BEV algorithm development and top-conference publications are preferred.
