Environmental Engineering - AI Data Trainer

Alignerr partners with leading AI research teams to build and train advanced AI models. The role involves developing complex environmental engineering problems, authoring technical solutions, and evaluating AI-generated plans for accuracy and compliance.

Responsibilities:

• Develop Complex Problems: Design advanced environmental engineering problems across domains like contaminant transport, mass balance in treatment plants, hydrology, and Life Cycle Assessments (LCA)
• Author Ground-Truth Solutions: Create rigorous, step-by-step technical solutions, including chemical dosage calculations, hydraulic flow models, and pollutant dispersion simulations that serve as 'golden responses.'
• Technical Auditing: Evaluate AI-generated remediation plans, environmental impact statements, and mathematical proofs for technical accuracy, safety, and adherence to regulatory standards (e.g., EPA, ISO 14001)
• Refine Reasoning: Identify logical fallacies in AI reasoning—such as incorrect stoichiometry in biological processes or failure to account for secondary environmental impacts—and provide structured feedback to improve the model's 'thinking' process

Requirements:

• Advanced Degree: Masters (pursuing or completed) or PhD in Environmental Engineering, Civil Engineering (with an environmental focus), or a closely related field
• Domain Expertise: Strong foundational knowledge in core areas such as aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation
• Analytical Writing: The ability to communicate complex ecological and engineering concepts clearly and concisely in written form
• Attention to Detail: High level of precision when checking unit conversions (e.g., mg/L to ppm), chemical equations, and regulatory compliance logic
• No AI experience required
• Prior experience with data annotation, data quality, or evaluation systems
• Familiarity with environmental modeling software