Environmental Engineering - AI Data Trainer

We partner with the world's leading AI research labs to build smarter, more accurate AI models — and we need environmental engineers to help get there. Your deep domain knowledge will directly shape how AI understands and reasons through complex environmental problems, from contaminant transport to regulatory compliance.

This is a unique opportunity to apply your technical expertise in a new frontier: making AI systems genuinely reliable in one of the most consequential fields on the planet.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

• Design Advanced Technical Problems — Create challenging, real-world environmental engineering scenarios spanning contaminant transport, mass balance in treatment plants, hydrology, Life Cycle Assessments (LCA), and more
• Author Ground-Truth Solutions — Develop rigorous, step-by-step technical solutions — including chemical dosage calculations, hydraulic flow models, and pollutant dispersion simulations — that serve as the gold standard for AI training
• Audit AI-Generated Outputs — Evaluate AI remediation plans, environmental impact statements, and mathematical proofs for technical accuracy, regulatory adherence (EPA, ISO 14001), and safety
• Refine AI Reasoning — Identify and document model failures such as incorrect stoichiometry, flawed mass balances, or missed secondary impacts, and provide structured feedback to improve how the model "thinks"
• Stress-Test Model Limits — Push AI systems to their boundaries across domains like water and wastewater treatment, air quality management, EHS compliance, and hazardous waste remediation

• Pursuing or holding a Master's or PhD in Environmental Engineering, Civil Engineering (environmental focus), or a closely related field
• Strong foundational knowledge in at least one core area: aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation
• Able to communicate complex technical and ecological concepts clearly in writing
• Highly precise — you catch unit conversion errors (mg/L to ppm), faulty chemical equations, and regulatory logic gaps
• Self-motivated and comfortable working independently and asynchronously
• No prior AI experience required

• Experience with data annotation, data quality assurance, or evaluation workflows
• Familiarity with environmental modeling software (e.g., AERMOD, SWMM, or similar)
• Background in regulatory frameworks such as EPA standards or ISO 14001 compliance

• Work on cutting-edge AI projects alongside top research labs and AI teams
• Fully remote and flexible — work on your own schedule, on your own terms
• Gain hands-on exposure to large language models and how they're trained and evaluated
• Freelance perks: autonomy, variety, and collaboration with a global network of experts
• Meaningful work — your contributions directly improve how AI handles environmental problems that matter
• Potential for ongoing work and contract extension