Advanced Physics Problem Expert (PhD/Postdoc)

About OpenTrain

OpenTrain is the centralized platform where people build careers in AI training and data labeling. We help freelancers discover projects, consolidate proof of work, and grow a durable portfolio that demonstrates real scientific contribution.

We hire and contract contributors directly for projects that shape how AI systems learn. This role is an opportunity to turn your physics expertise into high-impact training data for advanced models.

Why AI training work matters

AI training (data labeling / human feedback) is the human backbone of modern AI: experts produce high-quality examples that teach models to reason, compute, and explain. Contributors work remotely, often part-time, and directly influence how state-of-the-art systems behave.

• 100% remote work — contribute from anywhere with an internet connection.
• Flexible, part-time hours — fit work around research, teaching, or other commitments.
• Accessible and cutting-edge — domain expertise rather than prior AI experience is often the primary requirement.

The role

OpenTrain is recruiting a Physics Expert (PhD / Postdoc or advanced-stage PhD candidate) to produce rigorous solutions for advanced physics problems used to train generative AI. You will supply derivations, analyses, and reproducible computational checks that reflect research-quality standards.

This is a contractor, part-time engagement. Compensation is hourly (PAY_PER_HOUR) at $80–$140 USD/hr. The expected commitment is approximately 10 hours per week sustained over an 8–10 week project window. This role is open worldwide and work language is English.

• Role type: Contractor, Part-time
• Pay: $80–$140 USD per hour (hourly rate may vary within the posted range)
• Time commitment: ~10 hours/week for an 8–10 week period
• Location: Remote / worldwide; work language: English

What you'll do

• Solve advanced physics problems from your specialization and deliver clear, rigorous derivations.
• Produce technically precise written solutions using LaTeX for mathematical notation.
• Use SymPy, Python, and Jupyter notebooks for symbolic or numerical verification and reproducible checks.
• Identify subtleties in problem statements, handle special cases and boundary conditions, and flag ambiguities.
• Propose well-reasoned interpretations when statements are ambiguous and document assumptions clearly.
• Iterate on submitted solutions in response to reviewer feedback and maintain high standards of documentation.

Requirements

• PhD in physics or advanced-stage PhD candidacy with active research experience (required).
• Research expertise in at least one subfield (examples include High Energy, Mathematical Physics, Condensed Matter, AMO/Quantum Optics, Statistical Physics, Biophysics, Gravitation/Cosmology/Astrophysics, or Quantum Information).
• Proficiency with LaTeX for typesetting mathematics and with SymPy, Python, and Jupyter for verification and reproducible work.
• Demonstrated excellence in written technical communication and the ability to present clear, self-contained solutions.
• Availability to engage consistently for ~10 hours/week over an 8–10 week period.

Helpful background and how to apply

Ideal applicants have 2–5 recent representative publications (past ~5 years) with accessible arXiv or DOI records, familiarity with research workflows, and experience using computational tools for verification. You should be comfortable with iterative review and refining solutions based on feedback.

To apply, use your OpenTrain profile and include links to 2–5 representative publications, a brief CV, a short writing sample or LaTeX/Python notebook demonstrating your problem-solving style, and your availability for the project period. Selected contributors will collaborate with project reviewers to ensure solutions meet reproducibility and documentation standards.

• Suggested application materials: CV, 2–5 publication links (arXiv/DOI), short LaTeX/Python sample, and availability.