Data Annotator
Expert Technology Services
Certainly! Here's a job summary in list format based on your description for the Data Annotator & QA Reviewer (Autonomy & Robotics Mining Operations):
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### Job Summary
- Perform Manual Data Annotation & QA Review
- Annotate and QA review multi-modal data: video, images, 3D point clouds, and machine sensor data (LiDAR, radar, CAN bus).
- Label objects, 3D spatial boundaries, trajectories, actions, task sequences, intent, and outcomes for heavy mining equipment.
- Perception & Sensor Annotation
- Accurately label site entities (vehicles, personnel, terrain features) across 2D video and 3D LiDAR/radar data.
- Map and track heavy equipment trajectories, articulation, and operational states.
- Align and cross-reference visual and sensor telemetry for temporal and spatial consistency.
- Vision-Language-Action (VLA) Annotation
- Decompose mining workflows into structured sequences: task action intent causation outcome.
- Identify and annotate machine/operator intent and cause-effect chains within mining operations.
- Tag expected vs. actual outcomes (e.g., load success, hazard encounters).
- Quality Assurance (QA) & Data Audit
- Conduct rigorous QA audits for labeling accuracy, semantic consistency, and temporal coherence-even in edge cases (poor visibility, complex terrain).
- Provide structured feedback to annotators and suggest updates to labeling guidelines as edge cases are encountered.
- Collaboration & Process Improvement
- Work closely with cross-functional teams to maintain and improve annotation quality.
- Update and maintain documentation and annotation schemas for evolving mining scenarios.
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Required Experience & Skills
- 1+ years in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems.
- Proficient with 3D spatial data (LiDAR, depth maps, trajectories) and labeling tools (e.g., CVAT, Labelbox, Scale AI, Supervisely).
- Strong understanding of mining operations, spatial perception, and heavy equipment mechanics.
- Attention to detail, technical aptitude, and ability to handle structured metadata (JSON/XML).
- Strong communication skills for feedback and process documentation.
Desired/Bonus Experience
- Background in mining engineering, geotechnical engineering, robotics, autonomous vehicles, or industrial autonomy.
- Familiarity with autonomous haulage systems, telemetry logs, or embodied VLA models.
---
Let me know if you want a more condensed or detailed version!
---
### Job Summary
- Perform Manual Data Annotation & QA Review
- Annotate and QA review multi-modal data: video, images, 3D point clouds, and machine sensor data (LiDAR, radar, CAN bus).
- Label objects, 3D spatial boundaries, trajectories, actions, task sequences, intent, and outcomes for heavy mining equipment.
- Perception & Sensor Annotation
- Accurately label site entities (vehicles, personnel, terrain features) across 2D video and 3D LiDAR/radar data.
- Map and track heavy equipment trajectories, articulation, and operational states.
- Align and cross-reference visual and sensor telemetry for temporal and spatial consistency.
- Vision-Language-Action (VLA) Annotation
- Decompose mining workflows into structured sequences: task action intent causation outcome.
- Identify and annotate machine/operator intent and cause-effect chains within mining operations.
- Tag expected vs. actual outcomes (e.g., load success, hazard encounters).
- Quality Assurance (QA) & Data Audit
- Conduct rigorous QA audits for labeling accuracy, semantic consistency, and temporal coherence-even in edge cases (poor visibility, complex terrain).
- Provide structured feedback to annotators and suggest updates to labeling guidelines as edge cases are encountered.
- Collaboration & Process Improvement
- Work closely with cross-functional teams to maintain and improve annotation quality.
- Update and maintain documentation and annotation schemas for evolving mining scenarios.
---
Required Experience & Skills
- 1+ years in data annotation, labeling, or QA for computer vision, robotics, or autonomous systems.
- Proficient with 3D spatial data (LiDAR, depth maps, trajectories) and labeling tools (e.g., CVAT, Labelbox, Scale AI, Supervisely).
- Strong understanding of mining operations, spatial perception, and heavy equipment mechanics.
- Attention to detail, technical aptitude, and ability to handle structured metadata (JSON/XML).
- Strong communication skills for feedback and process documentation.
Desired/Bonus Experience
- Background in mining engineering, geotechnical engineering, robotics, autonomous vehicles, or industrial autonomy.
- Familiarity with autonomous haulage systems, telemetry logs, or embodied VLA models.
---
Let me know if you want a more condensed or detailed version!
Vacancy posted 1 day ago
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