Job Summary (List Format): Data Annotator & QA Reviewer Autonomy & Robotics (Mining)
Main Responsibilities:
- Execute manual data annotation and rigorous QA review for perception (video, images, sensor data) and VLA (Vision-Language-Action) models.
- Annotate and segment mining site entities (haul roads, berms, rock piles, vehicles, personnel, machinery) in 2D video and 3D LiDAR/radar data.
- Track and map heavy equipment trajectories, articulation angles, and operational states in complex mining environments.
- Align and cross-reference visual data with multi-sensor telemetry (LiDAR, radar, GPS/GNSS, IMU, CAN bus, payload sensors).
- Decompose mining tasks into granular actions, label machine/operator intent, and model chains of causation for operational behaviors.
- Tag and verify outcomes, comparing expected vs. actual results (e.g., full bucket loads, tire slips, dumping success).
- Conduct thorough QA audits to ensure high precision, especially in mining-specific edge cases (dust, mud, poor lighting, complex terrain).
- Audit temporal consistency and semantic accuracy in annotated datasets.
- Provide structured feedback and help update annotation guidelines as new mining scenarios emerge.
Key Skills & Requirements:
- 1+ years of professional experience in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems.
- Experience with 3D spatial data (LiDAR, depth maps, multi-camera feeds).
- Proficient in standard labeling platforms (CVAT, Labelbox, Scale AI, Supervisely, V7, Encord, etc.).
- Ability to systematically break down and label complex heavy machinery actions and interactions.
- Strong 3D spatial visualization and attention to detail, especially for mining environments and hazards.
- Working knowledge of mining operations, heavy equipment mechanics, and pit safety terminology.
- Comfortable with geospatial formats, sensor logs, and structured metadata (JSON/XML).
Preferred/Bonus Qualifications:
- Background in Mining Engineering, Geotechnical Engineering, Robotics, Autonomous Vehicles, or related fields.
- Experience with autonomous haulage systems (AHS), telemetry logs, or VLA models for industrial robotics.
Soft Skills:
- High attention to detail and precision in annotation.
- Effective communication and feedback skills for collaboration and guideline improvement.
- Ability to handle complex, ambiguous scenarios in high-stakes autonomous environments.
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Main Responsibilities:
- Execute manual data annotation and rigorous QA review for perception (video, images, sensor data) and VLA (Vision-Language-Action) models.
- Annotate and segment mining site entities (haul roads, berms, rock piles, vehicles, personnel, machinery) in 2D video and 3D LiDAR/radar data.
- Track and map heavy equipment trajectories, articulation angles, and operational states in complex mining environments.
- Align and cross-reference visual data with multi-sensor telemetry (LiDAR, radar, GPS/GNSS, IMU, CAN bus, payload sensors).
- Decompose mining tasks into granular actions, label machine/operator intent, and model chains of causation for operational behaviors.
- Tag and verify outcomes, comparing expected vs. actual results (e.g., full bucket loads, tire slips, dumping success).
- Conduct thorough QA audits to ensure high precision, especially in mining-specific edge cases (dust, mud, poor lighting, complex terrain).
- Audit temporal consistency and semantic accuracy in annotated datasets.
- Provide structured feedback and help update annotation guidelines as new mining scenarios emerge.
Key Skills & Requirements:
- 1+ years of professional experience in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems.
- Experience with 3D spatial data (LiDAR, depth maps, multi-camera feeds).
- Proficient in standard labeling platforms (CVAT, Labelbox, Scale AI, Supervisely, V7, Encord, etc.).
- Ability to systematically break down and label complex heavy machinery actions and interactions.
- Strong 3D spatial visualization and attention to detail, especially for mining environments and hazards.
- Working knowledge of mining operations, heavy equipment mechanics, and pit safety terminology.
- Comfortable with geospatial formats, sensor logs, and structured metadata (JSON/XML).
Preferred/Bonus Qualifications:
- Background in Mining Engineering, Geotechnical Engineering, Robotics, Autonomous Vehicles, or related fields.
- Experience with autonomous haulage systems (AHS), telemetry logs, or VLA models for industrial robotics.
Soft Skills:
- High attention to detail and precision in annotation.
- Effective communication and feedback skills for collaboration and guideline improvement.
- Ability to handle complex, ambiguous scenarios in high-stakes autonomous environments.
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