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AI Deforestation Early Warning Developer

Aaron Eriksson

Full-time · Job · London

About the role

By the time a deforestation event appears in a quarterly satellite mosaic, thousands of hectares may already be gone. Near-real-time detection from Sentinel-2 and Planet imagery — revisit cycles of 2 to 5 days — can catch clearing events within days of their start, enabling early intervention by conservation organizations and government enforcement agencies. We build the satellite-based forest monitoring system used by 12 conservation organizations and 4 national forestry agencies. Our change detection pipeline analyzes multispectral imagery across 80 million hectares of tropical forest continuously. The model achieves 89% recall on clearing events larger than 5 hectares; the priority is improving sensitivity to incremental clearing. You will redesign the change detection approach for small clearing events: better handling of cloud cover in tropical regions, temporal fusion across multiple passes to detect gradual clearing, and uncertainty quantification that lets the operations team prioritize their limited verification capacity.

Responsibilities

  • Improve change detection sensitivity for 0.5 to 5 hectare clearing events
  • Build cloud masking and gap-filling approaches for tropical multispectral imagery
  • Implement temporal fusion across multiple satellite passes for gradual clearing detection
  • Design uncertainty quantification for change detection results
  • Create evaluation benchmarks validated against field verification ground truth
  • Collaborate with conservation organizations on operational deployment requirements

Requirements

  • 5+ years in satellite remote sensing or geospatial ML with change detection experience
  • Experience with multispectral satellite imagery: Sentinel-2, Landsat, or Planet
  • Strong PyTorch skills and familiarity with geospatial deep learning libraries
  • Understanding of tropical forest ecology and clearing patterns
  • Ability to work with mission-driven conservation stakeholders

Benefits

  • Fully remote — EU or US time zones
  • Mission-driven conservation technology
  • 30 days PTO
  • Health insurance
  • Conference and research budget

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch computer vision aws spark

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