Environmental Engineering — AI Data Trainer

About the role

We are partnering with leading AI research teams to ensure large language models understand the physical and chemical world. As an Environmental Engineering – AI Data Trainer you will apply your expertise in water treatment, air quality, remediation and compliance to design, evaluate and improve AI‑generated environmental solutions.

Key responsibilities

• Design complex environmental engineering challenges, including contaminant transport, mass‑balance calculations, hydrology, air‑dispersion modeling and Life‑Cycle Assessments.
• Develop step‑by‑step ground‑truth solutions with chemical dosage, hydraulic flow and pollutant‑dispersion simulations to serve as benchmarks for AI evaluation.
• Audit AI‑generated remediation plans, impact statements and engineering calculations for technical accuracy, safety and regulatory compliance (EPA, ISO 14001, etc.).
• Identify model failures such as incorrect stoichiometry or missing secondary impacts and provide structured feedback to improve reasoning.
• Work independently on an asynchronous schedule, managing 10–40 hours per week.

Required profile

• Pursuing or holding a Master’s or PhD in Environmental Engineering, Civil Engineering (environmental focus) or a closely related discipline.
• Strong knowledge in at least one area: aquatic chemistry, wastewater process design, air‑quality engineering or hazardous‑waste remediation.
• Excellent written communication and meticulous attention to unit conversions, chemical equations and regulatory logic.
• Self‑directed, comfortable with independent, task‑based assignments. No prior AI experience required.

Required skills

• Aquatic chemistry
• Wastewater process design
• Air‑quality engineering
• Hazardous‑waste remediation
• Environmental modeling software (AERMOD, SWMM, MODFLOW)
• Data annotation and technical review processes
• Familiarity with EPA regulations and ISO 14001 standards

What we offer

• Fully remote, flexible schedule (10–40 hours/week).
• Opportunity to work on intellectually demanding problems at the intersection of AI and environmental science.
• Collaboration with a global team contributing to cutting‑edge AI research.
• Hands‑on exposure to how large language models are trained and evaluated.
• Freelance autonomy with potential for ongoing engagement.