Wildlife and Habitat Conservation Scientist
About this role
About the Role Your field knowledge of wildlife biology and habitat conservation is more valuable than ever — and not just in the field. We're looking for conservation scientists to help train and evaluate AI systems that reason about biodiversity, ecological health, and conservation strategy. This is a fully remote, flexible contract role where your scientific expertise directly shapes how AI understands the natural world. No prior AI experience required. - 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 - Assess the quality of ecological reasoning and proposed conservation strategies - Identify unrealistic assumptions, flawed methodologies, or misapplied conservation concepts - Provide clear, structured feedback that improves the ecological validity of AI outputs - Work independently and asynchronously — on your schedule, at your pace --- Who You Are - 3+ years of experience in wildlife biology, ecology, habitat conservation, or a closely related field - Strong working knowledge of biodiversity principles and ecosystem management - Able to critically evaluate applied ecological reasoning presented in written form - Comfortable reading and reviewing structured scientific content - Detail-oriented, self-motivated, and reliable when working independently Nice to Have - Graduate degree in Ecology, Wildlife Biology, Conservation Science, or a related discipline - Hands-on field research or conservation program experience - Familiarity with AI systems, content evaluation, or scientific annotation workflows --- Why Join Us - Meaningful impact — your expertise helps AI reason responsibly about conservation and the natural world - Fully remote and flexible — work from anywhere, on a schedule that fits your life - Cutting-edge work — gain direct exposure to how large language models are trained and evaluated - Autonomy — task-based structure means you're in control of your workflow - Global collaboration — connect with a diverse, expert community of scientists and researchers - Ongoing opportunity — strong contributors are considered for contract extension and future projects
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