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AI Crop Disease Detection Engineer

Aaron Eriksson

Full-time · Job · Singapore

About the role

The models you'll ship will scan over two million acres of farmland every growing season. You'll join our computer vision team of five, working alongside two research scientists who publish internally and expect implementations to be grounded in the latest literature on crop pathology detection. Our platform ingests drone and satellite imagery and flags early signs of blight, rust, and pest damage before they're visible to the naked eye. The gap we're closing is speed: by the time a farmer sees damage, treatment windows have often already closed. We expect you to read relevant papers, prototype quickly, and bring findings back to the team in our biweekly research review. This is applied work, not pure research — every model you build needs to run inference in the field within a growing season, not a publication cycle.

Responsibilities

  • Build computer vision models for early-stage crop disease detection
  • Implement and adapt findings from recent agricultural imaging research
  • Validate model performance against agronomist-labeled ground truth data
  • Present findings and prototypes at biweekly research reviews
  • Optimize inference pipelines for near-real-time field deployment

Requirements

  • Strong background in computer vision and convolutional architectures
  • Experience with PyTorch or TensorFlow in a production setting
  • Comfortable reading and implementing findings from academic papers
  • Prior work with satellite or drone imagery is a plus

Benefits

  • Fully remote with quarterly in-person research reviews
  • Competitive equity grant
  • Conference attendance and paper submission support
  • Health and dental coverage
  • Dedicated research time each week outside of delivery work

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Categories

AI & Machine Learning Engineers

Skills

python pytorch computer vision tensorflow docker

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Language

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AI Expertise

AI & Machine Learning Engineers

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