Environmental Engineering - AI Data Trainer
Alignerr · Remote · Research / analysis
Environmental Engineering - AI Data Trainer is an open role at Alignerr, available remotely, with pay listed as $35–$60/hr.
Applications open on Alignerr’s own site. How we make money.
At a glance
- Pay
- $35–$60/hrStated by Alignerr
- Location
- Remote
- Work type
- Research / analysis
- Field
- Science / STEM
- Listed
- Since Oct 4, last confirmed Oct 22
How the pay compares
Higher than 23% of the 117 distinct hourly rates advertised for Science / STEM roles right now. Hourly USD, top of each advertised range, one count per distinct rate. Same method as the Pay Index.
From Alignerr’s listing
“Environmental Engineering — AI Data Trainer About the Role We're partnering with the world's leading AI research labs to build smarter, more reliable AI models — and we need environmental engineers to make it happen.”
Show the full listing
Environmental Engineering — AI Data Trainer About the Role We're partnering with the world's leading AI research labs to build smarter, more reliable AI models — and we need environmental engineers to make it happen. Your deep technical knowledge of water treatment, air quality, waste management, and environmental compliance will directly shape how AI reasons through complex, real-world engineering problems. This is a rare opportunity to apply your academic and professional expertise in a new and impactful way, without leaving your desk. Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : 10–40 hours/week What You'll Do Design Advanced Engineering Problems — Create challenging, domain-rich scenarios spanning contaminant transport, mass balance in treatment systems, hydrology, air dispersion modeling, and Life Cycle Assessments (LCA) Author Ground-Truth Solutions — Develop rigorous, step-by-step technical solutions — including chemical dosage calculations, hydraulic flow models, and pollutant dispersion simulations — that serve as benchmark "golden 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, and more) Sharpen AI Reasoning — Identify logical flaws such as incorrect stoichiometry in biological treatment processes or overlooked secondary environmental impacts, then provide structured feedback to improve how models think Work Independently — Manage your own schedule and contribute asynchronously across a global team 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 one or more of: aquatic chemistry, wastewater process design, air quality engineering, or hazardous waste remediation Able to communicate complex technical and ecological concepts cle
About Alignerr: part of Labelbox. See the full profile →
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Frequently asked questions
How do I apply for the Environmental Engineering - AI Data Trainer role at Alignerr?
Applications go through Alignerr's own portal - HumanSourcer links to the listing and never collects applications or handles hiring. Alignerr's access model is "Open / self-serve" (Open roles / applications). This listing was first seen here on October 4, 2026 and was still live at the last check.
Is the Environmental Engineering - AI Data Trainer role at Alignerr remote?
Yes - Alignerr lists this role as remote.
What does the Environmental Engineering - AI Data Trainer role at Alignerr pay?
Alignerr lists this role's pay as $35–$60/hr, quoted from the listing rather than estimated.
What kind of work is Environmental Engineering - AI Data Trainer?
Research / analysis. AI training, evaluation, coding, writing and specialist review
Is Alignerr a legitimate AI-training platform?
Labelbox expert network - and that relationship is rated "Confirmed" here because it can be checked against labelbox.com rather than taken on Alignerr's word. Confidence describes how well the ownership is evidenced. It is not a rating of how the platform treats the people who work for it, which no public source covers reliably.
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