The job focuses on the critical task of data annotation and validation, which are essential for training the machine learning models that power autonomous driving systems. The core duties involve:
Video and Image Labeling: You'd be responsible for meticulously labeling objects in video footage and images captured by cameras on autonomous vehicles. This includes identifying and drawing bounding boxes or polygons around objects like traffic signs, lanes, pedestrians, and other vehicles. This process creates the labeled training data used to teach the car's perception system to "see" and understand its environment.
Data Synchronization and Management: You'll need to handle and organize large volumes of video data, ensuring it's properly synchronized and stored in a master database for future use by engineers and developers.
Quality Assurance: A key part of the job is ensuring the accuracy of the labels. This involves validating the work of other team members and providing support to resolve any issues they encounter during the annotation process.
This role requires a high degree of focus, attention to detail, and a foundational understanding of computer vision and autonomous technology. While it's an entry-level position, it's a crucial stepping stone into the field, as the quality of the data directly impacts the performance and safety of the autonomous system.