Data Annotator

Skills: Data Annotation

Job Duties: Key Responsibilities1. Mining Perception & Spatial Sensor Annotation3D Spatial Bounding & Segmentation: Annotate site entities (haul roads, berms, rock piles, ore benches, light vehicles, personnel, and machinery) across 2D video feeds and 3D LiDAR/radar point clouds. Heavy Equipment Trajectory Tracking: Map precise motion paths, articulation angles, bucket/blade orientations, and velocity vectors for heavy vehicles operating in constrained mining environments. Sensor Fusion Alignment: Cross-reference visual camera telemetry with heavy vehicle sensors (GPS/GNSS, IMU, CAN bus torque/hydraulic pressure, payload sensors) to maintain temporal and spatial alignment.2. VLA & Heavy Operational Behavior Mapping Mining Task & Action Decomposition: Segment complex operational workflows into granular actions (e.g., Bench Approach $ightarrow$ Spotting $ightarrow$ Bucket Dig Cycle $ightarrow$ Swing $ightarrow$ Hopper Dump $ightarrow$ Haul Cycle).Intent Identification: Identify and label machine and operator intent behind steering shifts, speed adaptations, and bucket/blade maneuvers (e.g., Yielding to light vehicle, Negotiating steep grade, Slippage recovery).Chain of Causation Modeling: Annotate environmental triggers and causal relationships specific to mining conditions (e.g., High dust reduced visibility $ightarrow$ Speed reduced; Oversized boulder detected in pit $ightarrow$ Trajectory rerouted).Outcome Verification: Tag expected vs. actual site outcomes (e.g., Full Bucket Load Achieved, Tire Torque Slip, Berm Encroachment, Dumping Clearance Succeeded).3. Quality Assurance (QA) & Audit Conduct rigorous QA audits on labeled mining datasets to enforce strict precision standards across edge cases (extreme dust, mud, nighttime/glare lighting, subterranean conditions).Audit temporal consistency in vehicle trajectory sequences and ensure correct semantic labeling of mining-specific hazards and terrain features. Provide structured feedback to internal annotators and external data partners, updating labeling schema guidelines as mining edge cases emerge.

Job Requirements: Qualifications & Requirements

Required Experience: 1+ years of professional experience in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems. Experience handling 3D spatial data (LiDAR point clouds, depth maps, spatial trajectories, multi-camera feeds).Working knowledge of standard labeling platforms (CVAT, Labelbox, Scale AI, Supervisely, V7, Encord, etc.).Ability to break down complex heavy-machinery interactions into structured sequence flows: Task $ightarrow$ Action $ightarrow$ Intent $ightarrow$ Causation $ightarrow$ Outcome. Key Technical & Soft SkillsDomain Literacy: Familiarity with mining operations, pit safety terminology, and heavy equipment mechanics (haulers, excavators, loaders).Spatial Perception: Strong 3D spatial visualization skills (understanding vehicle yaw/pitch/roll, bucket kinematics, and 3D point cloud depths).Attention to Detail: Meticulous approach to labeling tight bounding boxes and subtle terrain/hazard changes in poor visibility conditions. Technical Aptitude: Comfortable working with geospatial formats, sensor logs, and structured metadata formats (JSON/XML).