Producing high-fidelity annotations on imagery and video, the hybrid Data Annotation Specialist will label data to train machine learning models, ensuring adherence to project specifications and quality targets.
Key Responsibilities:
Label static imagery and video across various sensor types and scene conditions according to project specifications Produce various types of annotations, including bounding boxes, segmentation masks, and multi-object tracking annotations Review and correct model-assisted outputs while maintaining accuracy and throughput standards across large batches Required Qualifications: 1+ years of image or video annotation experience or equivalent precision work in a quality-managed production environment Familiarity with at least one professional annotation platform such as CVAT, V7 Darwin, or Labelbox Practical understanding of bounding boxes, segmentation masks, and object tracking Strong visual attention to detail and the ability to maintain standards across extensive datasets Comfortable working in remote-desktop or browser-based environments
Experience: 1 year required