Description du poste
The Computer Vision Annotator is responsible for accurately labeling and annotating image and video data to support the development and training of computer vision and machine learning models. This role plays a critical part in improving model performance by creating high-quality datasets used for object detection, image classification, segmentation, tracking, and other visual recognition tasks. Responsibilities: - Perform high-precision annotation on visual data using specialized tools (bounding boxes, polygons, key points, 3D cuboids, semantic/instance segmentation). - Interpret complex labeling taxonomies and project specifications. - Utilize advanced features of annotation tools such as CVAT, Label Studio, 3D Slicer, or Amazon SageMaker Ground Truth. - Proactively identify challenging data patterns, edge cases, and potential biases in datasets. - Contribute to the development of annotation best practices, workflow optimizations, and training materials. - Assist in basic data preparation tasks such as resizing images, converting data formats, or ensuring sensor data synchronization. - Assist in tracking key performance insights (KPIs) such as annotation throughput, precision, and recall.
Profil recherché
- Minimum of 2 years of experience in data annotation, data labeling, or quality assurance specifically for computer vision or Machine Learning projects. - Demonstrated proficiency with a variety of annotation techniques including bounding boxes, polygons, key points, 3D cuboids, and segmentation masks. - Extensive hands-on experience with industry-standard annotation tools like CVAT, label Studio, 3D Slicer, Supervisely. - Solid understanding of computer vision concepts and terminology (e.g., object detection, image classification, LiDAR, point clouds). - Familiarity with quality control processes such as consensus labeling, audit sampling, gold-task seeding, and inter-annotator agreement metrics. - Exceptional attention to detail with an unwavering commitment to accuracy and consistency. - Strong written and verbal communication skills in English.