Background

DDD believes talent has no boundaries--and opportunities shouldn’t either. In 2001, we saw the need to bring tech skills and living-wage work to men and women in underserved communities in Asia. It was here that DDD helped plant the seed for a socially responsible outsourcing practice known as impact sourcing.

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

• 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
• Identify recurring annotation errors or tool-related issues
• Escalate quality risks or inconsistencies in labeling standards
• Suggest improvements to tools, taxonomy, or workflow
• Communicate effectively with peers, QA teams, and stakeholders in English
• Document issues clearly and accurately

• 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

• 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