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Environmental Engineering – AI Data Trainer

We're partnering with the world's leading AI research teams to build smarter, more technically rigorous AI models — and we need environmental engineers to help get there.

As an AI Data Trainer, you'll put your deep domain knowledge to work stress-testing and improving how advanced language models reason through real-world environmental engineering problems. From contaminant transport to hazardous waste remediation, your expertise will directly shape the reliability of AI systems used by engineers, researchers, and policymakers worldwide.

This is a fully remote, flexible contract role — work on your own schedule, on problems that matter.

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

• Design Advanced Technical Problems — Create challenging environmental engineering scenarios spanning contaminant transport, mass balance in treatment plants, hydrology, Life Cycle Assessments (LCA), and more
• Author Gold-Standard Solutions — Develop rigorous, step-by‑step technical solutions — including chemical dosage calculations, hydraulic flow models, and pollutant dispersion simulations — that serve as benchmark responses for AI training
• Audit AI Outputs — Critically evaluate AI-generated remediation plans, environmental impact statements, and technical calculations for accuracy, safety, and regulatory compliance (EPA, ISO 14001, and others)
• Sharpen AI Reasoning — Identify where AI reasoning breaks down — incorrect stoichiometry, missed secondary impacts, flawed unit conversions — and provide structured feedback that directly improves model performance

• Pursuing or holding a Master's or PhD in Environmental Engineering, Civil Engineering (environmental focus), or a closely related field
• Strong foundational knowledge in one or more core areas: aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation
• Able to communicate complex technical and ecological concepts clearly and precisely in written form
• Detail‑oriented — meticulous when it comes to unit conversions, chemical equations, and regulatory logic
• Self‑motivated and comfortable working independently on technically demanding tasks
• No prior AI experience required

• Experience with data annotation, data quality review, or evaluation systems
• Familiarity with environmental modeling software (e.g., AERMOD, SWMM, or similar)

• Work on cutting‑edge AI projects alongside top AI research labs
• Fully remote and flexible — work when and where it suits you
• Freelance perks: autonomy, variety, and global collaboration
• Get rare, hands‑on exposure to how large language models are trained and evaluated
• Contribute to AI that could shape the future of environmental science and sustainability
• Potential for ongoing work and contract extension