AI Data Trainer – Environmental Engineering

About the role

We are building AI systems that tackle complex environmental challenges. As an AI Data Trainer you will apply your environmental engineering expertise to shape, evaluate, and improve advanced AI models that support water and wastewater treatment, air quality management, hazardous waste remediation, and compliance work.

Key responsibilities

• Design domain‑specific engineering problems covering contaminant transport, mass‑balance calculations, hydrology, Life‑Cycle Assessment and related topics.
• Author detailed, step‑by‑step ground‑truth solutions—including chemical dosage, hydraulic flow, and pollutant dispersion calculations—to serve as benchmark data for AI training.
• Audit AI‑generated remediation plans, impact statements and mathematical proofs for technical accuracy, regulatory compliance (EPA, ISO 14001) and safety.
• Identify logical errors in AI reasoning (e.g., incorrect stoichiometry or omitted secondary impacts) and provide structured feedback to improve model performance.

Required profile

• Pursuing or holding a Master’s or PhD in Environmental Engineering, Civil Engineering with an environmental focus, or a closely related discipline.
• Strong knowledge in at least one area such as aquatic chemistry, wastewater process design, air‑quality engineering, or hazardous‑waste remediation.
• Excellent written communication of technical and ecological concepts.
• Detail‑oriented with a keen eye for unit‑conversion errors, flawed equations and regulatory gaps.

Required skills

• Experience with environmental modelling tools (e.g., SWMM, AERMOD, EPA models).
• Familiarity with data annotation, data‑quality assurance and evaluation workflows.

What we offer

• Fully remote, asynchronous work with flexible hours (10‑40 h/week).
• Freelance contract providing autonomy, variety and global collaboration.
• Hands‑on exposure to large‑language‑model training and evaluation.
• Potential for ongoing work and contract extensions.