Appen
Appen

Data Processing Associate

TLDR

Work on Project Nectar to annotate roof geometry on 3D models, aiding in building repair and material development, with comprehensive training provided.

Project Overview You will be working on Project Nectar, a roof geometry annotation project that uses 3D models built from historical satellite data. Your annotations will help identify structural features of roofs at scale, supporting downstream applications in building repair and roofing materials development. No prior experience in geospatial work is required—comprehensive training will be provided. Job Details Project-Based | Full-Time Onsite work in Cavite Shifting schedules may apply Training provided Anticipated Start Date : April 30, 2026 Role Purpose
  • Annotate roof geometry on 3D models — marking planes, ridges, edges, and structural features 

  • You'll work through structured batches of models using a web-based annotation platform 

  • Apply classification labels to roof features following project-specific guidelines 

  • Guidelines define exactly what to label and how — reading them carefully is core to the job 

  • Navigate multi-view 3D scenes from satellite-derived data to accurately interpret structures 

  • Review your work before submission to catch errors and ensure completeness 

  • Self-QA is expected — catching your own mistakes before they reach review keeps quality high

  • Meet daily quality and productivity targets 

What we are looking for
  • High school graduate, senior high school graduate, or college level / graduate 

  • Based in the Cavite area and willing to work onsite 

  • Basic computer skills — typing, navigating web platforms, and using online tools 

  • Strong attention to detail and ability to follow instructions accurately 

  • Comfortable working with image-based and 3D annotation tasks 

  • Willing to undergo training and adapt to changing project guidelines 

  • Able to meet daily quality and productivity targets 

  • Good English communication skills, written and verbal 

  • Team player with a positive attitude and openness to feedback 

  • Willing to work on shifting schedules if required by the project 

Preferred — not required

  • Experience in BPO, data entry, content moderation, or annotation projects
  • Familiarity with online tools or AI / data-related tasks
  • Strong spatial awareness or interest in visual tasks — helpful for 3D annotation work 
  • No prior experience in 3D annotation or geospatial work is required. Full training will be provided to all selected candidates. 

     About Appen

    Appen has been a leader in AI training data for over 30 years. We specialise in human generated data to train, fine tune, and evaluate models across generative AI, large language models, computer vision, and speech recognition. Our AI assisted data annotation platform and global crowd of more than 1 million contributors in over 200 countries support model pre training, supervised fine tuning, evaluation and benchmarking, safety and red teaming, and multilingual global expansion.

    Why You’ll Love Working Here

    At Appen, we foster a culture of innovation, collaboration, and excellence. We value curiosity, accountability, and a commitment to delivering the highest quality AI solutions for frontier models.
    You’ll work on complex challenges that shape the future of AI across industries and geographies, alongside talented people in a culture that values humility over ego. You’ll have the flexibility to deliver in a way that works for you and your team, supported by tools, resources, and development opportunities to continue to build your capability over time.

    Appen is a global leader in high-quality data management services that support AI development for industry leaders. By leveraging a vast network of over 1 million crowd workers, we provide the human-curated data needed to enhance machine learning, speech recognition, and other AI capabilities.

    Founded
    Founded 1996
    Employees
    500+ employees
    Industry
    IT Services
    Total raised
    $59M raised
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