This job is no longer available
This job expired on 19/07/2026. It no longer accepts applications.
AI Data Trainer – Environmental Engineering
Alignerr
Job description
About the role
AI is being applied to solve complex environmental challenges, and we need expert engineers to guide its learning. As an AI Data Trainer you will create, evaluate, and refine domain‑specific problems that help large language models understand water treatment, air quality, hazardous waste and related regulations. This is a fully remote, flexible contract position.
Key responsibilities
- Design advanced engineering problems covering contaminant transport, mass balances, hydrology, life‑cycle assessments and more.
- Author detailed, step‑by‑step ground‑truth solutions including chemical dosage calculations, hydraulic flow models and pollutant dispersion simulations.
- Audit AI‑generated remediation plans, impact statements and mathematical proofs for technical accuracy and regulatory compliance (EPA, ISO 14001).
- Identify logical errors in AI reasoning and provide structured feedback to improve model performance.
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 such as aquatic chemistry, wastewater process design, air‑quality engineering or hazardous‑waste remediation.
- Excellent written communication and attention to detail, especially for unit conversions, chemical equations and regulatory logic.
- No prior AI experience required; training will be provided.
Required skills
- Aquatic chemistry
- Wastewater process design
- Air‑quality engineering
- Hazardous‑waste remediation
- Chemical dosage calculations
- Hydraulic flow modeling
- Pollutant dispersion simulation
- Environmental modeling software (e.g., SWMM, AERMOD, EPA models)
- Data annotation and quality‑assurance workflows
What we offer
- Fully remote, asynchronous work schedule (10–40 hours per week).
- Freelance contract with autonomy, variety and global collaboration.
- Hands‑on exposure to how large language models are trained and evaluated.
- Potential for ongoing work and contract extensions.
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