Wildlife and Habitat Conservation Scientist

Wildlife and Habitat Conservation Scientist (AI Training)

About The Role

We're looking for wildlife and habitat conservation scientists to help evaluate and improve AI systems trained on biodiversity protection and ecosystem management. Your field expertise will directly shape how AI understands and reasons about conservation science — making a real impact on one of the most important applications of AI technology.

• Organization: Alignerr (Powered by Labelbox)
• Type: Hourly / Task-based Contract
• Location: Remote
• Commitment: 10–40 hours/week

What You'll Do

• Review AI-generated wildlife and habitat conservation scenarios for scientific accuracy and ecological validity
• Assess the quality of ecological reasoning, species assessments, and conservation strategies produced by AI systems
• Identify unrealistic assumptions, flawed methodologies, or misapplied conservation concepts
• Provide clear, structured feedback that helps improve how AI models handle complex ecological topics
• Work independently and asynchronously on your own schedule

Who You Are

• 3+ years of hands-on experience in wildlife biology, ecology, or habitat conservation
• Strong working knowledge of biodiversity principles and ecosystem management practices
• Able to critically evaluate applied ecological reasoning presented in written form
• Comfortable reviewing and annotating structured scientific content
• Self-motivated and reliable with the ability to meet deadlines independently

Nice to Have

• Graduate degree in Ecology, Wildlife Biology, Conservation Science, or a related field
• Experience in field research or managing conservation programs
• Familiarity with AI systems or content evaluation workflows

Why Join Us

• Contribute your expertise to cutting-edge AI projects with real-world conservation implications
• Fully remote and flexible — work on your own schedule, from anywhere
• Freelance perks: autonomy, variety, and collaboration with a global network of subject-matter experts
• Gain meaningful exposure to how large language models are trained and evaluated
• Potential for ongoing work and contract extension