Contract work drawing 3D cuboid boxes around vehicles, pedestrians and obstacles in LiDAR scans for perception models. Suits people with strong spatial reasoning and a background in CAD, 3D modelling or GIS.
عن الوظيفة
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.
المصدر الرسمي: claru.ai. قد تتغير التفاصيل؛ والكلمة الأخيرة لصفحة المنصة.هل في هذه الوظيفة خطأ؟ أخبرنا.
كيف تُوظَّف لدى Claru المزيد من وظائف توسيم البيانات كل وظائف Claru ما يكسبه العاملون في توسيم البيانات كم تدفع Claru؟ هل Claru موثوقة؟
