Physics AI Solutions Evaluator

Overview

We are hiring physics experts to review and refine AI-generated physics solutions, mathematical derivations, and theoretical arguments. Your domain knowledge will help train next-generation AI systems by identifying errors, clarifying assumptions, and providing rigorous, actionable feedback.

This is a remote contractor opportunity that requires roughly 10 hours per week for 8–10 weeks and pays between $80 and $150 per hour depending on qualifications. Candidates located in the US, Canada, or the UK are especially encouraged to apply.

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. Contributors use OpenTrain to discover projects, build a unified AI training portfolio, and grow a durable freelance career in a fast-growing industry.

OpenTrain connects specialists with projects where human expertise directly shapes how AI systems behave. Creating an OpenTrain account is free and lets you apply in minutes.

Why AI training work matters

AI systems learn from examples and feedback prepared by people. Subject-matter experts who evaluate model outputs ensure those systems reason correctly, maintain scientific rigor, and perform reliably in real-world scientific contexts.

This role offers flexible, remote work that directly shapes advanced scientific applications of AI — no prior AI experience is required; your physics expertise is the essential qualification.

The role — what you'll do

You will critically evaluate AI-generated physics content and deliver structured, technical feedback that the project team uses to improve model behavior and training data quality.

Work will combine close reading of derivations with computational checks using symbolic and numerical tools to confirm or correct claims.

• Identify errors, unjustified steps, missing assumptions, dimensional inconsistencies, and logical weaknesses in solutions and derivations.
• Delineate substantive scientific issues from stylistic matters and provide precise written guidance for improvement.
• Articulate and document your reasoning for identified flaws and suggest clear, actionable fixes or alternative approaches.
• Recognize opportunities to optimize or sharpen arguments and recommend clarifications or reformulations.
• Independently verify or counter-check claims using LaTeX, SymPy, Python, and Jupyter notebooks when required.
• Deliver structured evaluation ratings and written comments to support iterative enhancement of submitted work.

Requirements

All listed requirements below come from the project and are mandatory for consideration. Provide evidence (links or files) for items that apply.

Candidates must have reliable access to high-speed internet and a computer capable of running Jupyter and standard scientific Python tools.

• PhD in physics with an active record of independent research in a specialized subfield (examples: High Energy, Mathematical, Biophysics, Statistical, Condensed Matter, AMO/Quantum Optics, Gravitation/Cosmology, Quantum Information, Optical Materials).
• Experience as a postdoctoral researcher, research fellow, junior/assistant professor, or senior research scientist.
• Recent (roughly within the last 5 years) representative publications in a relevant subfield; provide arXiv or DOI links.
• Advanced proficiency with LaTeX, SymPy, Python, and Jupyter for theoretical modeling and computational validation.
• Demonstrated experience reviewing others' scientific work (peer review, supervision, dissertation committees, or seminar leadership).
• Exceptional written communication skills able to convey nuanced, constructive, technically rigorous feedback.

Who should apply

You should apply if you are an active research physicist with a PhD, recent publications, and hands-on experience vetting technical work in your field.

No prior AI-specific experience is required — the project values deep subject-matter expertise, careful reasoning, and the ability to validate claims computationally.

Work details, pay, and how to apply

Engagement: contractor, part-time — approximately 10 hours per week for 8–10 weeks. Remote work; applicants based in the US, Canada, or the UK are encouraged to apply.

Pay: $80–$150 per hour (final rate within this range depends on experience and demonstrated expertise). Labeling type: evaluation/rating of text outputs.

To apply: create or sign in to your OpenTrain account and submit your profile, CV, PhD details, and representative publications (arXiv or DOI links). Include a short note describing your relevant reviewing experience and your weekly availability for the project.

• Engagement type: Contractor, Part-time.
• Time requirement: ~10 hours/week for 8–10 weeks.
• Languages: English required.
• Location: Remote; applicants in US, CA, GB encouraged.