Mathematics Expert (Python, Degree Required)

About OpenTrain

OpenTrain aggregates data-labeling and AI-training jobs from many companies and platforms so contributors can find this work in one place. Creating an OpenTrain account is free and applying takes only a few minutes.

About AI training (what this work is)

AI training — also called data labeling, annotation, or human feedback work — is the human side of building machine learning systems. Tasks include creating and reviewing text used to train and evaluate models. Contributors directly shape how models behave by producing high-quality examples and verified answers.

• Work is typically remote and flexible; many projects let you pick hours and workload.
• This role focuses on producing text data (problem statements and verified answers) used for training and fine-tuning generative models.

The Role

We are seeking a Mathematics Expert to design original, computationally intensive, research-style mathematics problems and provide fully verified answers with reproducible Python verification. Your work will be used for text generation, evaluation/rating, and fine-tuning datasets for AI systems.

This is a contract, part-time role requiring 20+ hours per week. Compensation is hourly: $15–$60 USD per hour (hourly rate listed as $60 USD). The project uses a custom/other labeling platform.

• Data type: TEXT; label types: TEXT_GENERATION, EVALUATION_RATING, FINE_TUNING.
• Employment: CONTRACTOR, PART_TIME.
• Time commitment: 20+ hours/week.

What You'll Do

Create original problem statements that reflect realistic mathematical research workflows and require non-trivial reasoning chains. Problems should span topics such as number theory, combinatorics, graph theory, and numerical analysis.

Provide complete, fully verified answers with reproducible verification code in Python, using standard mathematical libraries. Deliver clear, self-contained problem text and verification scripts so results can be rerun and validated.

• Design computationally intensive problems suitable for research-style evaluation.
• Write concise, unambiguous problem statements and expected outputs.
• Provide Python verification using libraries such as NumPy, SciPy, and SymPy.
• Include notes on computational complexity assumptions and numerical stability where relevant.
• Produce examples and edge-case tests demonstrating reproducibility.

Requirements

Applicants must meet all of the following mandatory requirements and provide a CV in English that indicates English proficiency and includes an email address and phone number.

• Bachelor’s degree or higher in Mathematics or a related field.
• 2+ years of relevant professional, research, or teaching experience.
• Strong Python skills with hands-on experience in NumPy, SciPy, and SymPy.
• Experience with computational and numerical methods and familiarity with computational complexity concepts.
• Hands-on text annotation or review experience and subject-matter familiarity with research-style computational mathematics problems.

Restricted Locations and Eligibility

This project cannot acquire contributors from certain countries, territories, and jurisdictions. Please confirm your eligibility before applying.

• Restricted locations for acquisition: Iran, Cuba, North Korea, Syria, Sudan, Venezuela, Myanmar, Russia, Belarus, Palestine
• Switzerland
• China, Taiwan
• Kenya
• States of the USA: Alaska, Arkansas, California, Connecticut, Delaware, Georgia, Hawaii, Illinois, Indiana, Kansas, Louisiana, Maine, Maryland, Massachusetts, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, Ohio, Oregon, Tennessee, Utah, Vermont, Washington, West Virginia
• Antarctica, Aruba, Åland Islands, Saint Barthélemy, Bonaire, Sint Eustatius and Saba, Bouvet Island, Cocos (Keeling) Islands, Democratic Republic of the Congo, Cook Islands, Christmas Island, Western Sahara, Falkland Islands (Malvinas), French Guiana, Guadeloupe, South Georgia and the South Sandwich

How to Apply

To apply, create an OpenTrain account (free), then submit your CV in English. Your CV must state your English proficiency level and include an email address and phone number. Applications will be reviewed for required qualifications and eligibility.

We will evaluate candidates based on academic background, relevant experience, Python verification samples, and prior annotation or review work. Shortlisted candidates may be asked to complete a paid sample task.

• Include examples or links to Python-verified mathematical work if available.
• Be prepared to provide a short sample problem with a runnable verification script if requested.