Material Science Expert (Masters/PhDs)
Alignerr · Remote · Data annotation / labeling
Material 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 22
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
“Material Science Expert (AI Training) About the Role What if your deep expertise in semiconductor materials or molecular modeling could directly shape how AI reasons through some of the most complex problems in modern science?”
Show the full listing
Material Science Expert (AI Training) About the Role What if your deep expertise in semiconductor materials or molecular modeling could directly shape how AI reasons through some of the most complex problems in modern science? We're looking for Materials Science specialists — Masters and PhD holders — to help train and evaluate cutting-edge AI models, ensuring they think with the rigor and depth that real scientific work demands. This is a fully remote, flexible contract role built for researchers and domain experts who want to do meaningful, high-impact work on their own schedule. No prior AI experience needed — just serious materials science knowledge and a passion for precision. Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : 10–40 hours/week What You'll Do Develop, solve, and review advanced materials science problems with real-world scientific relevance Apply your expertise in semiconductor materials, molecular modeling, or related specializations to design complex, high-quality problem sets Evaluate AI-generated responses for scientific accuracy, depth, and reasoning quality Collaborate asynchronously with AI researchers and fellow domain experts to push model performance forward 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 Materials Science or a closely related field Deep expertise in semiconductor materials, molecular modeling, or adjacent disciplines Comfortable coding in Python or MATLAB for research or analytical work Exceptionally detail-oriented with strong written communication skills in English Self-motivated and reliable when working independently in an async environment Based in the U.S., Canada, U.K., Australia, or New Zealand Nice to Have Experience with data annotation, evaluation systems, or quality assurance workflows Background in AI, computational materials science, or related techni
About Alignerr: part of Labelbox. See the full profile →
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Frequently asked questions
How do I apply for the Material 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 Material Science Expert (Masters/PhDs) role at Alignerr remote?
Yes - Alignerr lists this role as remote.
What does the Material 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 Material 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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