Environmental Engineer - AI Model Training – Remote
Job Type: Contractor Location: Remote Role Description If you’re an environmental engineer who thrives on technical precision, systems thinking, regulatory nuance, and real-world problem solving, this is a unique opportunity to contribute directly to how the next generation of AI systems reason about environmental challenges. In this role, you will challenge and evaluate advanced language models on environmental engineering topics. Your work will help strengthen model reasoning, improve technical accuracy, and ensure AI systems communicate environmental engineering concepts responsibly and clearly. Your expertise as an Environmental Engineer will directly help improve the world’s premier AI models and play an important role in shaping the future of AI. Your Profile
- 4+ years of professional experience in environmental engineering, with significant hands-on work in areas such as site remediation, wastewater treatment, stormwater management, air quality, hazardous waste, environmental permitting, or environmental compliance.
- Deep knowledge of environmental engineering principles, including contaminant fate and transport, treatment systems, pollution control technologies, environmental sampling, and risk-based decision making.
- Proven experience reviewing technical reports, engineering calculations, compliance documentation, design assumptions, environmental data, or regulatory submissions.
- Bachelor’s degree in Environmental Engineering, Civil Engineering, Chemical Engineering, or a closely related engineering field; a master’s degree, PE license, or specialized environmental certification is preferred.
- Previous experience with AI data training, annotation, or evaluating AI-generated technical content is a strong plus.
Key Responsibilities
- Evaluate AI-generated responses for technical accuracy, regulatory soundness, engineering judgment, and practical feasibility across environmental engineering topics.
- Challenge advanced language models with complex, job-specific environmental engineering scenarios involving remediation, water treatment, air quality, waste management, permitting, and environmental risk assessment.
- Review and refine AI-generated prompts, solutions, calculations, explanations, and responses to ensure they reflect sound environmental engineering practice.
- Provide structured feedback that identifies reasoning gaps, incorrect assumptions, missing constraints, unsafe recommendations, or oversimplified technical conclusions.
- Develop and review benchmark tasks that test model performance on real-world environmental engineering workflows, including design tradeoffs, compliance interpretation, and field-based decision making.
- Shape AI communication standards by helping models explain environmental engineering concepts with clarity, precision, and appropriate caveats.