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Plant Biologist

Remote · New Zealand Full-time

Job Title: Plant Biologist Job Type: Contract Location: Remote Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. Key Responsibilities:

  • Conduct detailed research on plant biology, genetics, and cellular processes for AI training purposes.
  • Collect, analyze, and document plant samples from diverse environments.
  • Collaborate with interdisciplinary teams to translate biological insights into actionable data for AI models.
  • Design and implement experiments to investigate plant responses to various environmental factors.
  • Integrate principles of biology, chemistry, and geology to enhance data accuracy and project outcomes.
  • Prepare high-quality written and verbal communications to articulate findings and recommendations.
  • Contribute to the development of datasets and frameworks for AI applications in life sciences.

Required Skills and Qualifications:

  • Bachelor’s degree or higher in Biology, Chemistry, or a related field (Master’s or PhD preferred for advanced research roles).
  • Demonstrated expertise in plant biology, genetics, and molecular techniques.
  • Strong foundations in sample collecting, data analysis, and experimental design.
  • Proficiency in integrating cross-disciplinary knowledge from chemistry and geology.
  • Excellent written and verbal communication skills, with a focus on scientific reporting.
  • Experience working in laboratory settings and with relevant research equipment.
  • Ability to work independently and as part of a distributed, remote team.

Preferred Qualifications:

  • Rubric experience or familiarity with data annotation for AI training.
  • Prior experience contributing to projects at the intersection of biology and artificial intelligence.
  • Published research in peer-reviewed journals or contributions to scientific datasets.

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