AI Data Labeler — 2D/3D Vision Annotations

Innodata; United States; Remote; USD 42,000 - 63,000; Full time

Job summary

Innodata is seeking detail-oriented image and video annotators to produce high-fidelity annotations for machine learning models. You will work across real-world and synthetic imagery, applying 2D, 3D, and tracking annotations while following strict ontology and guidelines.

Role requires familiarity with annotation platforms (CVAT, V7 Darwin, Labelbox, Scale) and keen attention to detail to maintain consistency across thousands of frames.

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 (CVAT, V7 Darwin, Labelbox, Scale, or comparable).
• Understanding of bounding boxes, segmentation masks, keypoints, and object tracking; know what makes each correct.
• Strong visual attention to detail and ability to maintain standards across thousands of frames.
• Ability to follow labeling guidelines exactly and ask precise questions when unclear.
• Comfortable working in remote-desktop or browser-based environments.

Responsibilities

• Label static imagery and video across various sensor types and conditions.
• Create 2D bounding boxes, segmentation masks, and keypoints to spec.
• Produce oriented/rotated bounding boxes as scenes require.
• Create 3D/6-DOF annotations when needed.
• Maintain object identity in multi-object tracking across sequences.
• Review and correct model-assisted outputs and report recurring issues.
• Adhere to project ontology and guidelines; avoid out-of-ontology objects.
• Meet accuracy and throughput targets across large batches.
• Incorporate QA feedback to rework similar cases.

Skills

Image annotation

Video annotation

Platform familiarity

Attention to detail

Remote-desktop work

Tools

CVAT

V7 Darwin

Labelbox

Scale

Job description

Innodata is seeking detail-oriented image and video annotators to produce high-fidelity annotations for machine learning models. You will work across real-world and synthetic imagery, applying 2D, 3D, and tracking annotations while following strict ontology and guidelines.

Role requires familiarity with annotation platforms (CVAT, V7 Darwin, Labelbox, Scale) and keen attention to detail to maintain consistency across thousands of frames.