LiDAR 3D Annotation & Data Labeling Specialist

At Mindy Support, we are a global leader in data annotation and business process outsourcing, powering cutting-edge AI and machine learning solutions for Fortune 500 companies and fast-growing tech innovators. We foster a collaborative, remote-first environment where detail-oriented professionals can build long-term tech-adjacent careers. We are currently looking for LiDAR 3D Annotation & Data Labeling Specialists to join our team on a long-term project focused on 3D LiDAR cuboid annotation and spatial segmentation. High-performing contributors will gain priority access to advanced, higher-paying autonomous vehicle and spatial AI projects. What You’ll Do 3D Point Cloud Bounding Box Annotation: Fit tight 3D cuboids around objects (vehicles, pedestrians, cyclists, static structures) across frame sequences with high spatial accuracy. 3D Semantic Segmentation: Label individual points within dense point clouds to define complex environmental geometry with zero gaps or overlaps. Multi-Sensor QA & Verification: Review, refine, and audit AI-generated 3D bounding boxes and sensor fusion alignments (LiDAR overlaid with 2D camera feeds). Object Tracking & Trajectory Consistency: Track dynamic objects across multi-frame LiDAR scenes, ensuring accurate pitch, roll, yaw, and heading vector consistency. What We’re Looking For Experience: Minimum 6+ months of hands-on experience in 3D LiDAR point cloud annotation, 3D segmentation, or multi-sensor data labeling. Tool Proficiency: Proven expertise using 3D spatial software such as Segments.ai, BasicAI, Cognic, Scale AI, CVAT, or equivalent platforms. Quality Standards: Ability to maintain a 95%+ accuracy rate, strictly adhering to tight cuboid boundary rules, point-count density thresholds, and occlusion handling. Precision: Ability to segment visually verifiable 3D spatial geometry objectively without unverified assumptions. Workflow Efficiency: Skilled in using software shortcuts and hotkeys to execute 3D sequence workflows while running background screen-recording tools. Professional Mindset: Reliable, detail-oriented, and comfortable working in a structured, quality-driven environment. Onboarding & Certification Process Training & Practice: Review spatial guidelines, master hotkeys, and practice on sample 3D point cloud datasets. Benchmark Test: Annotate 3–5 3D LiDAR tasks within quality and speed benchmarks. Paid Certification: Complete a ~1-hour onboarding process (paid upon entry to production tasks). Production: Access ongoing paid project batches immediately upon passing certification. Project & Payment Details Work Schedule: 25–40 hours per week (long-term contract, though occasional short idle times may occur). Payment Methods: PayPal, Bank Transfer, or Payoneer. Equipment Requirements: Stable internet connection, a capable PC/laptop for 3D rendering, and screen-recording software compatibility.