Forestry and Land Management Scientist (AI Training)
- Toronto, Ontario, Canada
- Télétravail
- Publié 10 sept. 2026
- 1 poste
30 $ US–55 $ US / heure
Ouvre un site externe
- Type d’emploi
- Contrat
- Niveau d’expérience
- Intermédiaire · 2+ ans
- Formation minimale
- Baccalauréat
- Postuler avant le
- 10 oct. 2026
- Langue de l’offre
- anglais
- Heures de travail
- 40 heures par semaine
- Exigences de lieu
- Country, Toronto, Ontario, Canada
- Niveau d’expérience
- Mid-Senior level
Résumé du poste
Review and evaluate forestry and land management scenarios to improve the accuracy of AI training datasets. Provide expert feedback to identify factual errors and refine AI-generated content regarding forest health and sustainability.
Détails du poste
About The Role We're looking for experienced forestry and land management scientists to help shape how AI understands sustainable forest management, land-use planning, and environmental decision-making. Your field expertise will directly influence the accuracy and reliability of AI systems used by researchers, practitioners, and policymakers worldwide. This is a fully remote, flexible contract role — work on your own schedule while contributing to cutting-edge AI development. Organization: Alignerr (Powered by Labelbox) Type: Hourly / Task-Based Contract Location: Remote Commitment: 10–40 hours/week What You'll Do Review and evaluate forestry and land management scenarios used in AI training datasets Assess the accuracy of AI-generated content related to forest health, land use, and sustainability practices Identify factual errors, oversimplifications, or flawed management recommendations in AI outputs Provide structured, expert feedback to improve applied environmental reasoning in AI systems Work independently and asynchronously to complete task-based assignments on your own schedule Who You Are 3+ years of hands-on experience in forestry, land management, or a closely related field Strong working knowledge of forest ecosystems, silviculture, and sustainable land-use practices Able to critically evaluate applied environmental decision-making and management recommendations Comfortable reading and reviewing technical written content with precision Self-motivated and reliable when working independently in a remote setting Nice to Have Degree in Forestry, Natural Resources, Environmental Science, or a related discipline Experience with land-use planning, conservation programs, or regulatory compliance Familiarity with AI tools, content evaluation workflows, or data annotation platforms Why Join Us Work on cutting-edge AI projects with top research labs and technology organizations Fully remote and flexible — set your own hours and work from anywhere Freelance perks: autonomy, variety, and collaboration with a global network of experts Make a real impact by ensuring AI systems reflect accurate, real-world forestry science Potential for ongoing work and contract extension based on performance
Ce que vous ferez
Review and evaluate forestry and land management scenarios to improve the accuracy of AI training datasets. Provide expert feedback to identify factual errors and refine AI-generated content regarding forest health and sustainability.
Exigences
Requires 3+ years of hands-on experience in forestry or land management with strong knowledge of silviculture and ecosystems. Candidates must be able to critically evaluate technical content and work independently in a remote setting.
Avantages
• Autonomy • Flexible schedule • Global networking opportunities
Compétences indiquées
- Technical · Souhaitée
- Sens de l’organisation · Souhaitée
- Training · Souhaitée
- Collaboration · Souhaitée
- Health · Souhaitée
- Compliance · Souhaitée
- Planning · Souhaitée
- management · Souhaitée
- Decision Making · Souhaitée
- Accuracy · Souhaitée
- Flexible · Souhaitée
- discipline · Souhaitée
Autres compétences pertinentes
Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.
- Forestry
- Land Management
- Sustainable Forest Management
- Land-use Planning
- Silviculture
- Environmental Decision-making
- Content Evaluation
- Data Annotation
- Technical Writing
- Environmental Reasoning
Domaines d’emploi
- Science & Research
- Environmental & Sustainability
- Agriculture
- Technology
- Data & Analytics
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