Atmospheric Science Expert (Masters/PhDs)
Alignerr · Remote · Data annotation / labeling
Atmospheric Science Expert (Masters/PhDs) is an open role at Alignerr, available remotely, with pay listed as $75–$90/hr.
Applications open on Alignerr’s own site. How we make money.
At a glance
- Pay
- $75–$90/hrStated by Alignerr
- Location
- Remote
- Work type
- Data annotation / labeling
- Field
- Science / STEM
- Listed
- Since Oct 4, last confirmed Oct 23
How the pay compares
Higher than 55% 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
“Atmospheric Science Expert (AI Training) About the Role What if your deep expertise in atmospheric science could directly shape how AI understands climate, weather systems, and the natural world?”
Show the full listing
Atmospheric Science Expert (AI Training) About the Role What if your deep expertise in atmospheric science could directly shape how AI understands climate, weather systems, and the natural world? We're looking for Atmospheric Science experts to develop and evaluate advanced scientific problems that train cutting-edge AI models — making AI smarter, more rigorous, and more scientifically grounded. This is a fully remote, flexible contract role built for researchers, academics, and scientists who want to contribute meaningfully to the future of AI — on their own schedule. Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : 10–40 hours/week What You'll Do Design, solve, and review advanced atmospheric and environmental science problems with real-world relevance Apply your expertise in climate modeling, ecology, sustainability, or related areas to craft complex, rigorous problem sets Evaluate AI-generated scientific reasoning for accuracy, depth, and methodological soundness Collaborate asynchronously with AI researchers and fellow domain experts to improve AI model performance Ensure scientific rigor, clarity, and precision across all deliverables Work independently on task-based assignments — fully on your own schedule Who You Are Master's or PhD in Atmospheric Science, Environmental Science, or a closely related field Deep expertise in at least one of: climate modeling, atmospheric dynamics, ecology, or sustainability science Proficient in Python or R for scientific research or data analysis Exceptionally detail-oriented — you hold your work to the highest scientific standards Clear and precise written communicator who can explain complex concepts accessibly Fluent in English and based in the U.S., Canada, U.K., Australia, or New Zealand Nice to Have Prior experience with data annotation, data quality review, or AI evaluation workflows Background in interdisciplinary environmental research or science communication Familiarity with AI tools, l
About Alignerr: part of Labelbox. See the full profile →
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Frequently asked questions
How do I apply for the Atmospheric Science Expert (Masters/PhDs) 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 Atmospheric Science Expert (Masters/PhDs) role at Alignerr remote?
Yes - Alignerr lists this role as remote.
What does the Atmospheric Science Expert (Masters/PhDs) role at Alignerr pay?
Alignerr lists this role's pay as $75–$90/hr, quoted from the listing rather than estimated.
What kind of work is Atmospheric Science Expert (Masters/PhDs)?
Data annotation / labeling. 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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