Physics AI Research Expert — Mathematical Modeling & Derivations

About OpenTrain

OpenTrain is the #1 platform for people building careers in AI training and data labeling. We connect specialists with projects that shape how AI systems learn, and help contributors consolidate work history and grow a professional portfolio in this fast-growing field.

Why AI training matters

AI training (also called data labeling or human feedback work) is the human foundation of modern AI: people create, correct, and evaluate examples that models learn from. Contributors do remote, flexible work that directly influences model reasoning and reliability across science, engineering, and everyday applications.

• Work fully remote and flexible — fit tasks around research, teaching, or other work.
• Contribute high-impact, cutting-edge outputs that shape how scientific reasoning appears in AI systems.

The role

OpenTrain is recruiting a Physics AI Research Expert to provide deep subject-matter contributions that teach AI systems to reason about advanced physics problems. This part-time contractor role expects publication-quality mathematical derivations, computational modeling, and clear, structured explanations suitable for AI training and evaluation.

• Employment type: Contractor, Part-time.
• Time commitment: Less than 20 hours per week.
• Location: Must be based in the US, UK, or Canada and fluent in English.

What you'll do

You will apply your active physics research skills to create, verify, and improve training materials that teach models how to solve and explain complex physics problems.

• Formulate, solve, and precisely document advanced mathematical physics problems using rigorous methodology.
• Develop and validate computational models and simulations in Python, SymPy, and Jupyter.
• Produce publication-level written explanations and derivations in LaTeX-ready form.
• Review, critique, and improve existing problem solutions to enhance accuracy and clarity for training data.
• Communicate technical results and edge cases with project coordinators and the AI technical team.

Requirements

Candidates must meet the role's core academic and technical standards listed below. These are firm requirements for consideration.

• PhD in physics or current senior PhD student status with active, ongoing research.
• 2–5 representative publications in a relevant physics subfield within the last 5 years.
• Expertise in mathematical modeling, derivations, and system proofs at publication quality.
• Proficiency with LaTeX, Python, SymPy, and Jupyter notebooks.
• Exceptional written and verbal communication: clear, accurate explanations of intricate concepts.
• Ability to work independently and execute research tasks with minimal supervision.

Helpful background

The project favors candidates with interdisciplinary experience that eases transfer of domain expertise into AI training datasets.

• Previous participation in AI training, RLHF, or interdisciplinary computational research projects.
• Recognition as a contributor, reviewer, or thought leader within your physics community.

Compensation, data types, and next steps

Pay is hourly, between 80 and 110 USD per hour depending on experience and task complexity. Work will involve text-generation, evaluation/rating, and data-collection style tasks that produce technical content and assessments used to train models.

If your background matches the requirements, prepare representative publications and examples of recent derivations or Jupyter notebooks for the application process. OpenTrain and the OpenTrain platform will coordinate onboarding and task assignments.

• Pay: PAY_PER_HOUR, USD 80–110/hr (listed hourlyRate 110 USD).
• Labeling/data types: TEXT_GENERATION, EVALUATION_RATING, DATA_COLLECTION.
• Required language: English. Countries: US, GB, CA.