Computer Vision Data Annotator - Polygon Segmentation, Keypoints & COCO JSON

We are seeking experienced Data Annotators to support an agricultural computer vision project involving polygon segmentation and keypoint annotation. Project Overview The project focuses on annotating agricultural field images for AI model training. Selected annotators will be responsible for creating high-quality annotations following detailed guidelines and quality standards. Annotation Types Polygon Segmentation Instance Segmentation Keypoint Annotation COCO JSON Export Classes Crop Broad Leaf Weed Grass Weed In addition to segmentation, growth-point keypoints will be annotated for weed instances. Requirements Applicants should have experience with one or more of the following: Polygon Segmentation Instance Segmentation Keypoint Annotation COCO JSON format Computer Vision datasets Agricultural datasets (preferred but not required) Experience with annotation platforms such as: CVAT Label Studio DataPlus SuperAnnotate Roboflow Similar annotation tools Responsibilities Follow annotation guidelines precisely Create accurate object boundaries Maintain consistency across images Deliver high-quality annotations Participate in quality review cycles Address feedback promptly Preferred Qualifications Previous work on AI training datasets Experience with segmentation-heavy projects Strong attention to detail Ability to work independently Good communication skills Selection Process Shortlisted candidates may be asked to complete a small evaluation task to assess annotation quality and guideline adherence. Successful annotators may receive additional work opportunities based on performance. To Apply, Please Include Your experience with polygon segmentation projects. Annotation tools you have used. Experience working with COCO JSON. Estimated daily annotation capacity. Examples of similar projects completed (if available). Your availability over the next two weeks. We are looking for reliable annotators who can consistently deliver accurate work and meet project timelines.