About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We hire and contract contributors to create, review, and validate data that modern AI systems learn from. Creating an OpenTrain account is free.

Why AI training work matters

AI training (data labeling/annotation and human feedback) is the human side of building intelligent systems. Contributors shape model behavior by creating examples, writing prompts and responses, and validating outputs. This work is remote, flexible, accessible, and lets you directly influence cutting-edge AI systems.

The role — what you'll do

You will design, author, and verify original computational STEM and machine-learning problems for model training and evaluation. Tasks combine text-based problem statements, coding challenges, and validated solutions that require non-trivial reasoning and reproducible Python code.

• Create computational STEM/ML problems that reflect real scientific or ML workflows.
• Write problem statements, expected inputs/outputs, and acceptance criteria in clear written English (C1+).
• Implement and run Python solutions (NumPy, Pandas, SciPy, scikit-learn, etc.) to validate answers and produce reproducible outputs.
• Produce examples for text generation, SFT-style prompt/response pairs, question answering, and evaluation/rating tasks.
• Prepare coding tasks and reference implementations; ensure problems are computationally intensive and not easily solvable by hand.

Key project details

This is contract, part-time project work with variable phases. Typical active phases expect approximately 10–20 hours per week of contribution. Work is remote and project-based; engagements are not permanent.

• Pay: hourly, up to $40/hour (posted range $15–$40/hr).
• Data type: text (problem statements, prompts, solution code, evaluation rubrics).
• Label types: text generation, question answering, evaluation/rating, and programming/coding content.

Requirements — what you must have

The project requires senior-level, hands-on expertise in machine learning, statistics, and Python-based data science. Preserve and follow these requirements exactly when applying.

• 5+ years of hands-on machine learning experience with demonstrated business impact.
• Expert Python for data science: NumPy, Pandas, SciPy, scikit-learn (statsmodels a bonus).
• Proven ability to design original computational STEM/ML problems with clear solution paths.
• Experience verifying/validating solutions with reproducible Python code and correct outputs.
• Expert statistical analysis and strong understanding of ML algorithms and practical trade-offs.
• Strong SQL skills: joins, aggregations, window functions, and database data manipulation.
• Practical experience with GenAI approaches (LLMs, RAG, prompt engineering, vector DBs).
• Familiarity with MLOps and deployment practices (packaging, reproducibility, basic monitoring).
• Experience with at least one modern ML framework (TensorFlow or PyTorch; LangChain is a bonus).
• Written English proficiency at C1+ level (or equivalent), comfortable writing clear documentation.
• Availability to contribute approximately 10–20 hours/week during active project phases.

Who should apply

Apply if you are an experienced ML practitioner who enjoys crafting challenging, reproducible computational problems and producing clear, validated solutions. Ideal applicants are comfortable switching between technical coding, statistical reasoning, and concise technical writing.

• Senior ML engineers, research scientists, or data scientists with production experience.
• People who can write reproducible Python notebooks and produce deterministic outputs.
• Contributors who are used to documenting assumptions, test cases, and validation steps.