Environmental Engineering - AI Data Trainer

Alignerr Corp.; Denver (CO); Remote; USD 83,000 - 131,000; Full time

Benefits offered by this job

Fully remote collaboration

Flexible schedule

Contract extension potential

Job summary

Alignerr is seeking an Environmental Engineering AI Data Trainer to shape how AI models reason about environmental problems. You will design advanced challenges, craft rigorous solutions, and help ensure data quality and regulatory relevance.

This remote hourly contract offers flexible hours (10–40 per week), collaboration with leading AI research teams, and the chance to apply environmental expertise to cutting‑edge model training without leaving your field.

Qualifications

• Master's or PhD in Environmental Engineering, Civil Engineering with environmental focus, or closely related discipline.
• Strong foundational knowledge in environmental topics and regulatory logic.
• Clear written communication of complex technical concepts.

Responsibilities

• Design technically rigorous environmental engineering challenges spanning contaminant transport, mass balance, hydrology, and LCA.
• Author detailed, step-by-step solutions including dosage calculations and hydraulic models.
• Audit AI outputs for technical accuracy, safety, and regulatory compliance.
• Identify and report errors in AI reasoning and provide structured feedback.

Job description

Environmental Engineering — AI Data Trainer

About The Role

We're partnering with the world's leading AI research labs to build smarter, more technically rigorous AI models — and we need environmental engineers to make it happen. Your domain expertise will directly shape how AI understands and reasons through complex environmental problems, from contaminant transport modeling to regulatory compliance.

This is a unique opportunity to contribute to cutting-edge AI development without leaving your field. No AI experience required — just deep environmental engineering knowledge and a sharp analytical mind.

• Organization: Alignerr
• Type: Hourly Contract
• Location: Remote
• Commitment: 10–40 hours/week

What You'll Do

• Design Advanced Problems — Develop technically rigorous environmental engineering challenges spanning contaminant transport, mass balance in treatment plants, hydrology, and Life Cycle Assessments (LCA)
• Author Ground-Truth Solutions — Write detailed, step-by-step technical solutions — including chemical dosage calculations, hydraulic flow models, and pollutant dispersion simulations — that serve as gold-standard reference answers
• Audit AI Outputs — Critically evaluate AI-generated remediation plans, environmental impact statements, and engineering calculations for technical accuracy, safety, and regulatory compliance (EPA, ISO 14001, and others)
• Sharpen AI Reasoning — Identify logical errors in AI responses — such as incorrect stoichiometry, flawed mass balances, or missed secondary environmental impacts — and provide structured feedback to improve model performance

Who You Are

• Holds or is pursuing a Master's or PhD in Environmental Engineering, Civil Engineering (with environmental focus), or a closely related discipline
• Strong foundational knowledge in one or more of: aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation
• Able to communicate complex technical and ecological concepts clearly in writing
• High attention to detail — especially with unit conversions, chemical equations, and regulatory logic
• Self-motivated and comfortable working independently and asynchronously
• No prior AI experience required

Nice to Have

• Experience with data annotation, quality evaluation, or technical review workflows
• Familiarity with environmental modeling software (e.g., AERMOD, SWMM, EPA tools)
• Background in EHS compliance or environmental impact assessment

Why Join Us

• Work on meaningful AI projects with top-tier research labs and teams
• Fully remote and flexible — set your own schedule and workload
• Bring your real-world expertise to a field that rarely gets deep technical scrutiny
• Gain exposure to how advanced large language models (LLMs) are trained and evaluatedFreelance perks: autonomy, variety, and global collaboration
• Potential for ongoing work and contract extension