Environmental Engineering – AI Data Trainer
About The Role
What if your environmental engineering expertise could help shape how the world's most advanced AI thinks about water treatment, air quality, and hazardous waste remediation
At Alignerr, we partner with leading AI research labs to build smarter, safer AI models — and we need domain experts like you to make it happen. As an Environmental Engineering AI Data Trainer, you'll stress-test cutting-edge language models, expose their blind spots, and help train them to reason correctly across complex environmental systems.
This is a fully remote, flexible contract role where your technical depth directly influences the quality of next-generation AI.
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
What You'll Do
- Design Challenging Problems — Develop advanced environmental engineering scenarios across contaminant transport, mass balance in treatment systems, hydrology, LCA, and more
- Author Gold-Standard Solutions — Write rigorous, step-by-step technical solutions — from chemical dosage calculations to hydraulic flow models — that serve as the benchmark for AI responses
- 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, etc.)
- Sharpen AI Reasoning — Identify flawed logic in AI responses — such as incorrect stoichiometry, missing secondary impacts, or improper unit conversions — and provide structured feedback to improve how models think
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 at least one of: aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation
- Able to communicate complex technical concepts clearly and precisely in writing
- Detail-oriented — you catch unit conversion errors, broken stoichiometry, and regulatory gaps that others miss
- No prior AI or data annotation experience required
Nice to Have
- Experience with data annotation, data quality review, or evaluation frameworks
- Familiarity with environmental modeling software (e.g., AERMOD, EPANET, SimaPro)
Why Join Us
- Work at the frontier of AI development alongside top research teams
- Fully remote and asynchronous — work when and where it suits you
- Freelance flexibility with the potential for ongoing contract work
- Rare opportunity to apply deep technical expertise in a high-impact, non-traditional role
- Contribute to AI systems that will reason about real-world environmental challenges
