Environmental Engineering, AI Data Trainer
Alignerr
Mumbai, Maharashtra, IndiaFresher21m agovia LinkedIn
This job at a glance
- Who can apply
- Freshers
- Must know
- Auditing
- Work
- Remote
- Pay
- Not stated by the employer.
- Listed
- 28 Oct 2026 · still open
Sign in free to see which of these skills your resume already shows.
About the Role
- Organization: Alignerr
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
At Alignerr, we partner with leading AI research labs to build smarter, safer AI models. As an Environmental Engineering AI Data Trainer, you will 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.
Key Responsibilities
- 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 (e.g., EPA, ISO 14001).
- 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.
Qualifications
- 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).