Role Overview

• The Operator Level 1 is responsible for executing 2D and 3D LiDAR annotation and segmentation tasks in accordance with defined SOPs, quality benchmarks, and productivity targets. This role requires technical precision, spatial awareness, and disciplined execution in high-volume production environments.

 Responsibilities

Production & Quality Execution

• Execute repetitive 2D/3D LiDAR annotation and segmentation tasks in strict adherence to SOPs
• Maintain classification accuracy across object types and categories

Meet or exceed defined benchmarks for:

• Productivity
• Quality
• Accuracy
• Sustain consistency in output with minimal supervision
• Issue Identification & Continuous Improvement
• Identify recurring annotation errors or tool-related issues
• Escalate quality risks or inconsistencies in labeling standards
• Suggest improvements to tools, taxonomy, or workflow

Communication & Collaboration

• Communicate effectively with peers, QA teams, and stakeholders in English
• Document issues clearly and accurately

Success Profile

• High attention to detail
• Strong spatial and logical reasoning ability
• Ability to sustain accuracy in repetitive workflows
• Foundational understanding of quality control
• Ability to identify misclassification and segmentation inconsistencies

Qualifications

Education Requirements

Diploma or higher qualification in a relevant field such as:

• Computer Science
• Information Technology
• Engineering (Electrical, Computer, Geospatial, or related)
• Data Science
• Geospatial Studies
• Or equivalent technical discipline

Technical Competencies

LiDAR & Segmentation Skills

• Working knowledge of 2D LiDAR annotation
• Working knowledge of 3D point cloud annotation

Systems & Communication

• Proficient working knowledge of a computer/laptop
• Strong English reading comprehension
• Ability to write clear and accurate English
• Ability to interpret and execute complex SOP documentation
• Ability to perform basic object segmentation and classification
• Understanding of bounding boxes, cuboids, and object tagging principles
• Ability to follow annotation taxonomies and ontology guidelines accurately

Additional Information

• Familiarity with annotation tools such as CVAT, SuperAnnotate, and Labelbox.
• Understanding of ML metrics, data quality principles, and AV/ADAS ecosystems.