LiDAR 3D Annotation & Data Labeling Specialist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a 3D Annotation & Data Labeling Specialist based in Canada.

This role offers the opportunity to contribute to advanced AI and machine learning projects through highly accurate 3D spatial data annotation.

You will work extensively with LiDAR point clouds, creating precise 3D bounding boxes and semantic segmentation across complex environments.

Your work will help improve the quality of datasets used to train and evaluate autonomous vehicles and spatial AI systems.

The position combines detailed annotation, multi-sensor quality assurance, and object tracking across sequential 3D scenes.

You will operate within structured quality standards where precision, consistency, and adherence to annotation guidelines are essential.

High-performing contributors may gain priority access to more advanced and higher-paying autonomous vehicle and spatial AI projects.

This is a strong opportunity for detail-oriented professionals seeking long-term, technology-focused contract work.

Accountabilities

Create accurate 3D bounding boxes around vehicles, pedestrians, cyclists, static structures, and other relevant objects across LiDAR frame sequences.

Perform detailed 3D semantic segmentation by labeling individual points within dense point clouds while avoiding gaps, overlaps, and inaccurate classifications.

Review and refine AI-generated 3D annotations to ensure they meet established spatial accuracy and quality standards.

Conduct multi-sensor quality assurance by validating LiDAR annotations against 2D camera feeds and checking sensor-fusion alignment.

Track dynamic objects consistently across multiple LiDAR frames, maintaining accurate pitch, roll, yaw, and heading information.

Apply strict cuboid boundary, point-density, occlusion, and spatial annotation guidelines throughout production workflows.

Maintain a minimum target accuracy of 95% while balancing precision with project productivity requirements.

Use annotation-platform shortcuts and hotkeys effectively to complete 3D sequence workflows efficiently.

Follow established procedures for screen recording and other project monitoring requirements.

Complete required training, practice exercises, benchmark tasks, and certification activities before entering production.

Maintain consistent quality and reliability across ongoing project batches and contribute to a structured, quality-driven production environment.

Requirements

• At least 6 months of hands-on experience with 3D LiDAR point cloud annotation, 3D semantic segmentation, or multi-sensor data labeling.
• Demonstrated proficiency with 3D annotation and spatial-data platforms such as Segments.ai, BasicAI, Cognic, Scale AI, CVAT, or equivalent tools.
• Strong understanding of 3D spatial geometry and the ability to interpret point-cloud data objectively without relying on unverified assumptions.
• Proven ability to maintain high annotation accuracy, with experience working toward or achieving 95%+ quality standards preferred.
• Familiarity with 3D bounding boxes, object tracking, occlusion handling, spatial segmentation, and multi-frame consistency.
• Ability to accurately interpret object orientation and maintain consistency across pitch, roll, yaw, and heading vectors.
• Strong attention to detail and the ability to identify subtle spatial inconsistencies in dense datasets.
• Comfortable working with structured guidelines, quality benchmarks, productivity expectations, and repetitive precision-focused workflows.
• Ability to use software shortcuts and hotkeys efficiently within 3D annotation environments.
• Reliable, organized, and capable of working independently while maintaining consistent output quality.
• Stable internet connection and access to a capable PC or laptop suitable for 3D rendering and annotation workloads.
• Compatibility with required screen-recording software and project monitoring tools.

Benefits

• Long-term contract opportunities with an expected workload of 25–40 hours per week.
• Ongoing paid project batches following successful certification.
• Paid onboarding and certification process, with approximately one hour of onboarding paid upon entry into production tasks.
• Structured training covering spatial annotation guidelines, workflows, and productivity shortcuts.
• Opportunity to work on advanced AI, autonomous vehicle, and spatial intelligence projects.
• High-performing contributors may receive priority access to more advanced and higher-paying projects.
• Flexible remote work environment.
• Payment available through PayPal, bank transfer, or Payoneer.
• Opportunity to build long-term experience in AI data annotation and technology-adjacent work.
• Access to a structured quality-focused workflow designed to support consistent professional development.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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