#49916 LiDAR 3D Annotation & Data Labeling Specialist at Mindy
Mindy is hiring a #49916 LiDAR 3D Annotation & Data Labeling Specialist in Remote. Remote.
About Mindy
Mindy Support provides end-to-end AI data infrastructure for enterprise AI/ML teams, including data collection, annotation, evaluation, and LLM training. It also offers data annotation for LLM and customer service/BPO. The company works with domain experts across industries such as healthcare, automotive, and fintech, and supports multilingual data services in 85+ languages with a global network of contributors.
#49916 LiDAR 3D Annotation & Data Labeling Specialist job description
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
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3D Point Cloud Bounding Box Annotation: Fit tight 3D cuboids around objects (vehicles, pedestrians, cyclists, static structures) across frame sequences with high spatial accuracy.
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3D Semantic Segmentation: Label individual points within dense point clouds to define complex environmental geometry with zero gaps or overlaps.
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Multi-Sensor QA & Verification: Review, refine, and audit AI-generated 3D bounding boxes and sensor fusion alignments (LiDAR overlaid with 2D camera feeds).
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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
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Experience: Minimum 6+ months of hands-on experience in 3D LiDAR point cloud annotation, 3D segmentation, or multi-sensor data labeling.
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Tool Proficiency: Proven expertise using 3D spatial software such as Segments.ai, BasicAI, Cognic, Scale AI, CVAT, or equivalent platforms.
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Quality Standards: Ability to maintain a 95%+ accuracy rate, strictly adhering to tight cuboid boundary rules, point-count density thresholds, and occlusion handling.
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Precision: Ability to segment visually verifiable 3D spatial geometry objectively without unverified assumptions.
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Workflow Efficiency: Skilled in using software shortcuts and hotkeys to execute 3D sequence workflows while running background screen-recording tools.
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Professional Mindset: Reliable, detail-oriented, and comfortable working in a structured, quality-driven environment.
Onboarding & Certification Process
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Training & Practice: Review spatial guidelines, master hotkeys, and practice on sample 3D point cloud datasets.
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Benchmark Test: Annotate 3–5 3D LiDAR tasks within quality and speed benchmarks.
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Paid Certification: Complete a ~1-hour onboarding process (paid upon entry to production tasks).
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Production: Access ongoing paid project batches immediately upon passing certification.
Project & Payment Details
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Work Schedule: 25–40 hours per week (long-term contract, though occasional short idle times may occur).
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Payment Methods: PayPal, Bank Transfer, or Payoneer.
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Equipment Requirements: Stable internet connection, a capable PC/laptop for 3D rendering, and screen-recording software compatibility.
