3D Object Detection Labeler

You'll annotate 3D point cloud data captured by LiDAR sensors, placing tight cuboid bounding boxes around objects like vehicles, pedestrians, cyclists, and static obstacles. Data originates from autonomous vehicle test drives and robotics platforms. Each scene requires correctly classifying object type, estimating orientation/heading, and flagging occluded or truncated objects. You'll work in tools like Scale AI's Lidar annotator, Segments.ai, or a custom internal web app - all browser-based with no local install needed.

Precision matters more than speed here. A misaligned cuboid or incorrect heading estimate on a vehicle directly degrades downstream perception model performance. You'll follow per-project annotation specs that define minimum point density thresholds for labeling, handling rules for partially visible objects, and class hierarchies. Disagreements with QA reviewers are resolved through an async comment thread, and you're expected to respond and correct within 24 hours.

This role suits people with spatial reasoning skills and experience in CAD, 3D modeling, GIS, or prior autonomous vehicle dataset work. Compensation is at the Expert tier given the technical precision involved. High-volume contributors with strong QA scores are eligible for senior labeler status, which includes project lead responsibilities and a pay bump.

Skills

• LiDAR point cloud interpretation
• 3D cuboid annotation
• Spatial reasoning
• Object classification
• Occlusion handling
• Annotation tool proficiency (Segments.ai / Scale LiDAR)

Where you can work

This role is 100% remote: you can work from anywhere in the world.