Synergiq

Data Annotator
LIDAR / 3D POINT CLOUD
Job title Data Annotator — LiDAR / 3D Point Cloud
Reports to Director of Operations
Location Hybrid – Washington, DC; Remote
Employment type Full Time ROLE SUMMARY
Produce high-fidelity annotations on LiDAR point clouds and fused sensor data used to train and evaluate 3D perception models. Annotators work to a defined ontology and labeling specification, meet project quality and throughput targets, and operate inside the designated 3D annotation environment for each program. Assignments rotate across projects, sensor configurations, and customers as program needs change.
KEY RESPONSIBILITIES
• Label LiDAR point clouds across a range of sensor types (spinning, solid-state, airborne, terrestrial), point densities, ranges, and scene conditions, including both real-world and synthetic data.
• Produce 3D bounding boxes (cuboids) with accurate position, dimensions, heading, and 6-DoF pose to project specification, including on sparse, partially occluded, or long-range objects.
• Produce per-point semantic and instance segmentation labels on point clouds, and polyline/polygon annotations for ground-plane features such as lanes, curbs, and boundaries.
• Produce multi-object tracking annotations across LiDAR sequences, maintaining consistent object identity and stable cuboid dimensions through occlusion, sweep exit and re-entry, and ego-motion.
• Work with fused camera–LiDAR views and calibration projections to disambiguate objects that are unclear in the point cloud alone; flag suspected calibration, timestamp, or sensor-alignment issues.
• Review, correct, and accept or reject model-assisted and pre-labeled 3D output; report systematic pre-label failure modes rather than silently correcting the same error sweep by sweep.
• Work strictly to the project ontology and guidelines; escalate ambiguous, out-of-ontology, or geometrically uncertain objects rather than guessing.
• Meet assigned accuracy and throughput targets, and hold that standard consistently across large sequences and batches.
• Complete rework promptly from QA feedback, applying the correction to comparable cases in the same batch.
• Log edge cases and recurring ambiguities (e.g., ground-plane thresholds, cuboid fit on truncated objects, reflectivity artifacts) so they can be adjudicated and folded into the guidelines.
• Follow all customer data-handling, confidentiality, and information security requirements for the assigned project, and keep required training current.
REQUIRED QUALIFICATIONS
• 1–2 years of LiDAR or 3D point cloud annotation experience, or equivalent 3D precision work in a quality-managed production environment.
• Working familiarity with at least one professional 3D annotation platform (for example Segments.ai, Scale, Deepen AI, Kognic, Supervisely, CVAT 3D, Labelbox, or comparable).
• Practical understanding of 3D cuboids, point cloud segmentation, and sequence tracking — and of what makes each one correct rather than merely present.
• Working understanding of point cloud characteristics: sparsity at range, occlusion and shadowing, reflectivity/intensity, ground-plane behavior, and ego-motion effects on sequences.
• Comfort navigating 3D scenes (rotating, panning, multi-view and bird's-eye inspection) for extended periods without loss of accuracy.
• Strong spatial attention to detail and the discipline to hold a standard across thousands of sweeps.
• Ability to follow written labeling guidelines exactly, and to ask precise questions when they are silent on a case.
• Comfortable working in remote-desktop or browser-based environments.
PREFERRED QUALIFICATIONS
• Experience with camera–LiDAR sensor fusion and multi-sensor annotation workflows.
• Experience with airborne or mobile-mapping LiDAR, radar, or thermal/IR imagery in addition to automotive-style scans.
• Experience with long sequences and 3D multi-object tracking.
• Experience reviewing model-assisted 3D pre-labels in a human-in-the-loop pipeline.
• Basic familiarity with coordinate frames, sensor calibration concepts, or common point cloud formats (PCD, LAS/LAZ, bin).
• Prior work on government or regulated-industry programs.
Program eligibility. Some programs require eligibility for a government background investigation or credentialing. Assignment to those programs is contingent on meeting those requirements.