About OpenTrain and AI training work

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We hire and contract contributors to create, verify, and document high-quality training tasks that teach AI systems how to solve real problems.

AI training (also called data labeling or human-in-the-loop work) is the human side of building modern AI: people prepare examples, design problems, and evaluate model outputs so models learn useful, reliable behavior. These opportunities are remote, often flexible, and let you shape how state-of-the-art systems behave.

The role

You will design complex, deterministic, computationally intensive data science problems that simulate realistic end-to-end workflows across industries (telecom, finance, government, e-commerce, healthcare, etc.). Your scenarios will require non-trivial reasoning chains across ingestion, cleaning, EDA, feature engineering, modeling, validation, and deployment considerations.

This is a part-time contractor role (less than 20 hours/week) with pay of USD 50 per hour. Work is remote, recurring, and requires precise technical documentation and reproducible, verified solutions implemented in Python.

• Employment type: Contractor, part time; typical commitment under 20 hours/week.
• Compensation: USD 50 per hour, paid per agreement.
• You will produce runnable Python code, verification tests, problem statements, and business-context documentation for each task.

What you'll do day-to-day

Create end-to-end, deterministic tasks that cannot be solved manually within reasonable timeframes and that reflect realistic business contexts. Implement, test, and verify correct solutions in Python using standard data science libraries and document all steps so they can be used to evaluate and train AI systems.

• Design realistic industry scenarios and precise problem statements with business context and success criteria.
• Implement full solutions in Python (pandas, numpy, scipy, scikit-learn, statsmodels) and include reproducible notebooks or scripts.
• Incorporate big-data and scalability considerations and note where distributed or optimized approaches are required.
• Provide verification artifacts: unit tests, example inputs/outputs, and clear explanation of the correct answer and edge cases.
• Ensure problems demand multi-step reasoning across the data science pipeline (ingestion → cleaning → EDA → modeling → validation → deployment).

Qualifications

You must meet the substantive technical and educational requirements below; we rely on these to ensure tasks are rigorous, reproducible, and industry-relevant.

• Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, or a closely related quantitative field.
• At least 5 years of hands-on data science experience with proven business impact (industry, consulting, or similar).
• Expert Python skills for data science, including pandas, numpy, scipy, scikit-learn, and statsmodels.
• Strong proficiency in SQL and database operations for large-scale data manipulation and analysis.
• Deep understanding of statistical analysis and machine learning algorithms, including assumptions, limitations, and practical use cases.
• Demonstrated ability to design deterministic, computationally intensive problems that span the full data science pipeline.
• Experience with GenAI technologies (LLMs, RAG, prompt engineering, vector databases) and familiarity with MLOps concepts and model deployment workflows.
• Working knowledge of modern AI/ML frameworks such as TensorFlow, PyTorch, and LangChain.
• Advanced English (C1 or higher) with strong technical writing skills for clear, structured problem statements and solutions.
• Reliable laptop/desktop, stable internet connection, and sufficient availability to take on recurring project tasks.

Location restrictions & eligibility

This role is remote but candidates cannot be based in the following locations: Iran, Cuba, North Korea, Syria, Sudan, Venezuela, Myanmar; Switzerland; China, Taiwan, Kenya; Armenia, Israel, Kazakhstan, UAE, Netherlands, Serbia, Kyrgyzstan, Turkey, Uzbekistan, Belarus, Russia, Ukraine, Abkhazia, South Ossetia; US states: 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 Islands, Heard Island and McDonald Islands, British Indian Ocean Territory, Northern Mariana Islands, Martinique, New Caledonia, Norfolk Island, Niue, French Polynesia, Saint Pierre and Miquelon, Pitcairn, Réunion, Saint Helena, Ascension and Tristan da Cunha, Svalbard and Jan Mayen, Sint Maarten (Dutch part), French Southern Territories, Tokelau, United States Minor Outlying Islands, Holy See, Virgin Islands (British), Wallis and Futuna, Mayotte.

If you are eligible and meet the qualifications, you will be considered for recurring task design assignments and paid as a contractor for hourly work.

How to apply and what to expect

Apply with a resume/CV and a short portfolio or links to reproducible code samples (notebooks or GitHub) that demonstrate your ability to design and implement full-pipeline data science solutions. Include brief descriptions of at least two projects that show problem design, technical approach, and business impact.

Selection includes a technical screening and a short paid test task to demonstrate problem-design and verification skills. Successful contributors are offered recurring, part-time contracts and can expect to produce multiple vetted tasks per month depending on availability.

• Compensation: USD 50 per hour; contractor, paid per agreement.
• Typical weekly commitment: under 20 hours; schedules are flexible but must meet deadlines.
• Provide clear, reproducible code and documentation for each delivered task.