Computer Vision Annotator

About the role

We are looking for a remote Computer Vision Annotator to create high-quality labeled datasets that power object detection, classification, segmentation and 3‑D perception models. The role involves working with image, video and point‑cloud data to ensure precise and consistent annotations for machine‑learning projects.

Key responsibilities

• Draw accurate bounding boxes, polygons, polylines and key points on images and video frames.
• Create 3‑D cuboids and annotate LiDAR point‑cloud data for autonomous‑vehicle and robotics applications.
• Perform semantic and instance segmentation, labeling every pixel where required.
• Interpret detailed labeling taxonomies, resolve ambiguous cases and refine guidelines with data scientists.
• Use annotation platforms such as CVAT, Label Studio, 3D Slicer or Amazon SageMaker Ground Truth.
• Provide feedback on tool usability and workflow efficiency, and help develop best‑practice documentation.
• Identify edge cases, potential biases and report findings to improve model robustness.
• Assist with basic data preparation tasks like image resizing and format conversion.
• Track annotation KPIs such as throughput, precision and recall.

Required profile

• Minimum 2 years of hands‑on experience in computer‑vision data annotation or quality assurance.
• Strong understanding of computer‑vision concepts (object detection, classification, LiDAR, point clouds).
• Excellent attention to detail and ability to maintain consistency across large volumes of data.
• Fluent written and spoken English with clear documentation skills.

Required skills

• Proficiency with CVAT, Label Studio, 3D Slicer, Supervisely or Amazon SageMaker Ground Truth.
• Experience using bounding‑box, polygon, key‑point, 3‑D cuboid and segmentation‑mask annotation techniques.
• Familiarity with quality‑control methods such as consensus labeling, audit sampling and inter‑annotator agreement metrics.
• Knowledge of point‑cloud and LiDAR annotation workflows.