OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We hire and contract contributors who help teach and shape modern AI by producing high-quality training data, evaluations, and ground-truth examples.
This role is offered directly by OpenTrain AI. Contributors work on real projects that feed model training and evaluation pipelines across the industry while building flexible, remote careers in a fast-growing field.
Why this work matters
AI systems learn from human-created examples and expert-reviewed solutions. Writing well-posed, reproducible computational physics problems and verified Python solutions helps train and evaluate models used in education, research tooling, and scientific computing.
This type of contribution is remote-friendly, often part-time and flexible, and gives you a chance to influence how models reason about physics and numerical computation.
The role
You will design challenging, research-style computational physics problems and provide fully verified, reproducible solutions implemented in Python. Problems should reflect real research workflows across mechanics, electromagnetism, thermodynamics, quantum mechanics, and related areas.
Work is project-based and part-time. Tasks combine creative text generation (problem statements), technical coding (simulations and verified solutions), and evaluation (quality checks and revision based on QA feedback).
Create original problem statements that target research-style workflows and numerical methods.
Provide clear, well-documented code, numeric verification, and written solution explanations.
What you'll do (typical tasks)
Each project will include a brief and detailed annotation guidelines; you will follow these to produce deliverables that meet QA criteria. You may also rate and review other contributors' work as part of quality control and dataset curation.
Draft clear problem statements and expected outputs suitable for model training or evaluation.
Write and test Python code for numerical integration, Monte Carlo methods, eigenproblems, PDE discretizations, and related computations.
Package reproducible notebooks or scripts with seeded random states, test cases, and references.
Respond to QA feedback and revise problems/solutions to meet verification standards.
Perform evaluation ratings on peer submissions and contribute to fine-tuning datasets when requested.
Requirements
Candidates must meet all listed requirements and be comfortable working from written guidelines and QA notes.
2+ years of applied, research, or teaching experience in physics, computational physics, or a closely related field.
Bachelor’s degree or higher in Physics or a related discipline.
Advanced Python skills with scientific libraries such as NumPy, SciPy, SymPy; experience producing clean, documented code.
Experience with numerical simulation techniques (integration, Monte Carlo, discretization methods, eigenvalue solvers, etc.).
Hands-on text annotation or review experience and familiarity with following detailed annotation guidelines.
Proven ability to create reproducible solutions and to follow QA feedback iteratively.
Professional written and spoken English; strong technical writing skills required.
CV must be in English and indicate your level of English proficiency; include your email address and phone number on the CV.
Pay, schedule, and contract
This is part-time, contractor work. Typical weekly commitment is 20+ hours; projects vary in length and scope.
Pay is hourly. Compensation ranges from USD 15 to USD 60 per hour depending on scope and complexity; final rate is project-dependent and will be communicated with each posting.
Employment types: Contractor, Part-time.
Time requirement: 20+ hours per week (project-based assignments).
Labeling and task types include text generation, evaluation rating, and fine-tuning dataset preparation.
Eligibility, restricted locations, and how to apply
OpenTrain accepts global applicants but some countries and territories are restricted for acquisition reasons. Please review the restricted list below before applying.
To apply, submit your CV in English via the OpenTrain application flow and include your phone number and email address. In your CV state your English proficiency level and summarize relevant physics, simulation, and Python experience.
Do not apply if you reside in any restricted location listed below.
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