We're looking for environmental engineering experts to help train and improve cutting-edge AI models. Your domain knowledge will directly shape how AI reasons through complex problems in water treatment, air quality, waste management, and environmental compliance — making a real impact on how the next generation of AI understands our planet.
This is a fully remote, adaptable contract role. You set your schedule and work on your own terms.
Design Complex Problems — Craft advanced environmental engineering challenges spanning contaminant transport, mass balance in treatment plants, hydrology, and Life Cycle Assessments (LCA) to stress-test AI reasoning
Author Gold-Standard Solutions — Write rigorous, step-by-step technical solutions — including chemical dosage calculations, hydraulic flow models, and pollutant dispersion simulations — that serve as reference benchmarks for AI training
Technical Auditing — Evaluate AI-generated remediation plans, environmental impact statements, and mathematical proofs for accuracy, safety, and regulatory compliance (EPA, ISO 14001, and related standards)
Refine AI Reasoning — Identify logical flaws such as incorrect stoichiometry in biological processes or overlooked secondary environmental impacts, and provide structured feedback that directly improves model performance
Work Asynchronously — Complete task-based assignments independently on your own schedule
Who You Are
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 of: aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation
Able to communicate complex engineering and ecological concepts clearly in writing
High attention to detail — especi
📌 Environmental Engineering (Ciudad de México)
🏢 Alignerr
📍 Ciudad de México
Postulate a este anuncio
Muestra tus habilidades a la empresa, rellenar el formulario y deja un toque personal en la carta, ayudará el reclutador en la elección del candidato.