Environmental Engineering - AI Data Trainer

About the role

We're partnering with the world's leading AI research labs to make AI smarter — and we need environmental engineers to help get there. As an AI Data Trainer specializing in environmental engineering, you'll stress-test advanced AI models on complex technical problems, expose their reasoning gaps, and help build systems that can think like a domain expert. This is a fully remote, flexible contract role that lets you apply your deep technical knowledge in a new and impactful way — without leaving your field.

What You'll Do

• Design Challenging Problems — Develop advanced environmental engineering scenarios spanning contaminant transport, mass balance in treatment plants, hydrology, pollutant dispersion, and Life Cycle Assessments (LCA)
• Author Expert-Level Solutions — Write rigorous, step-by-step technical solutions — including chemical dosage calculations, hydraulic flow models, and remediation plans — that serve as ground-truth benchmarks for AI training
• Audit AI Outputs — Evaluate AI-generated environmental impact statements, remediation strategies, and engineering calculations for technical accuracy, safety, and regulatory compliance (EPA, ISO 14001, and more)
• Sharpen AI Reasoning — Identify logical errors such as incorrect stoichiometry, flawed unit conversions, or failure to account for secondary environmental impacts, and provide structured feedback to improve model performance
• Work Independently — Complete task-based assignments asynchronously on a schedule that works for you

Who You Are

• Holds or is pursuing a Master's or PhD in Environmental Engineering, Civil Engineering (environmental focus), or a closely related discipline
• Strong foundational knowledge in one or more core areas: aquatic chemistry, wastewater process design, air quality engineering, hazardous waste remediation, or environmental compliance
• Precise and detail-oriented — you catch unit conversion errors, chemical equation imbalances, and regulatory inconsistencies that others miss
• Able to communicate complex engineering concepts clearly and concisely in writing
• Self-motivated and comfortable working independently on technical tasks
• No prior AI experience required

Nice to Have

• Experience with data annotation, data quality, or technical evaluation workflows
• Familiarity with environmental modeling software (e.g., AERMOD, SWMM, MODFLOW)
• Background in EHS compliance, regulatory consulting, or environmental auditing