Environmental Engineering - AI Data Trainer

Alignerr is partnering with leading AI research labs to enhance AI models with environmental engineering expertise. The AI Data Trainer will utilize their domain knowledge to create and evaluate complex environmental engineering scenarios, ensuring AI's accuracy in understanding environmental issues.

Responsibilities:

• Design Advanced Technical Problems — Craft challenging, real-world environmental engineering scenarios across domains including contaminant transport, mass balance in treatment plants, hydrology, and Life Cycle Assessments (LCA)
• Author Ground-Truth Solutions — Develop rigorous, step-by-step reference solutions — including chemical dosage calculations, hydraulic flow models, and pollutant dispersion simulations — that serve as the gold standard for AI responses
• Audit AI Outputs — Critically evaluate AI-generated remediation plans, environmental impact statements, and mathematical proofs for technical accuracy, safety, and adherence to regulatory standards (EPA, ISO 14001, and others)
• Refine AI Reasoning — Identify logical errors in AI responses — such as incorrect stoichiometry in biological treatment processes or failure to account for secondary environmental impacts — and provide structured feedback to improve model performance
• Work Independently — Complete task-based assignments on a flexible, asynchronous schedule that fits your life

Requirements:

• 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 core areas: aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation
• Able to communicate complex technical and ecological concepts clearly and precisely in writing
• Detail-oriented — confident checking unit conversions (mg/L to ppm), balancing chemical equations, and verifying regulatory compliance logic
• Self-motivated and comfortable working independently on analytical tasks
• No prior AI experience required
• Experience with data annotation, data quality review, or evaluation workflows
• Familiarity with environmental modeling software (e.g., AERMOD, SWMM, EPANET)
• Background in EHS compliance, environmental permitting, or regulatory frameworks