Remote Conservation Scientist for AI Training & Evaluation

Alignerr Corp.; New York (NY); Remote; USD 34.000 - 69.000; Teilzeit

Benefits dieser Stelle

Fully remote

Flexible schedule

Freelance/contract work

Global collaboration

Zusammenfassung

Alignerr (Powered by Labelbox) is seeking a Wildlife and Habitat Conservation Scientist to help refine AI training for biodiversity protection and ecosystem management. This remote, hourly-contract role offers flexible scheduling and meaningful impact on conservation science.

The role invites experienced wildlife biologists and ecologists to review AI outputs, assess ecological reasoning, and provide structured feedback to improve AI understanding of conservation topics.

Qualifikationen

• 3+ years of hands-on wildlife biology, ecology, or habitat conservation experience.
• Strong understanding of biodiversity principles and ecosystem management.
• Ability to critically evaluate applied ecological reasoning in written form.
• Comfortable reviewing and annotating structured scientific content.
• Detail-oriented, reliable and self-motivated.
• No prior AI experience required.

Aufgaben

• Review AI-generated wildlife and habitat conservation scenarios for accuracy.
• Assess ecological reasoning, species assessments, and conservation strategies.
• Identify unrealistic assumptions or misapplied concepts.
• Provide structured feedback to improve AI outputs in your area.
• Work independently and asynchronously on your schedule.

Kenntnisse

Wildlife biology

Ecology expertise

Ecological reasoning evaluation

Detail-oriented

Ausbildung

Graduate degree in Ecology / Wildlife Biology / Conservation Science

Jobbeschreibung

Alignerr (Powered by Labelbox) is seeking a Wildlife and Habitat Conservation Scientist to help refine AI training for biodiversity protection and ecosystem management. This remote, hourly-contract role offers flexible scheduling and meaningful impact on conservation science.

The role invites experienced wildlife biologists and ecologists to review AI outputs, assess ecological reasoning, and provide structured feedback to improve AI understanding of conservation topics.