Computational Mathematics Scientific Problem Designer

About OpenTrain

OpenTrain AI is the hiring and contracting organization for this role. OpenTrain is the #1 platform for finding and building careers in AI training and data labeling, helping specialists discover projects, build a professional profile, and apply in minutes.

Creating an OpenTrain account is free, and your profile can help you document credible AI training experience as you develop a long-term portfolio.

About AI Training Work

AI training is the human side of building artificial intelligence. People with deep subject expertise create examples, solve problems, review outputs, and provide structured evaluations that help modern AI systems become more accurate and reliable.

In this role, your computational mathematics expertise will support the development and evaluation of advanced language models through rigorous problem design, scientific programming, and technical review.

The Role

OpenTrain is recruiting a Computational Mathematics Scientific Problem Designer to create and solve challenging scientific problems suitable for research-grade or graduate-level work. You will use Python to develop computational solutions, organize content by category and difficulty, and help maintain high standards through structured quality review.

This contractor, part-time opportunity requires 20+ hours per week and is open worldwide. English is required.

• Employment type: Contractor and part-time
• Time requirement: 20+ hours per week
• Work location: Worldwide
• Working language: English

What You'll Do

You will combine mathematical research, scientific coding, written solution development, and detailed evaluation of AI training materials. Your work will need to be technically correct, logically coherent, and clearly documented for collaborators, reviewers, and team leads.

• Develop original scientific problems grounded in computational mathematics.
• Create problems appropriate for research or graduate-level work.
• Write and validate computational solutions using Python and sound mathematical principles.
• Define problem categories, secondary tags, and appropriate difficulty levels.
• Review problems and solutions for accuracy, reliability, and logical coherence.
• Incorporate feedback from reviewers and team leads.
• Document mathematical reasoning clearly for collaborators and reviewers.

Requirements

This role requires advanced academic and technical expertise in computational mathematics. Candidates must meet the education, publication, programming, communication, and review-judgment requirements below.

• PhD or current PhD candidacy in computational mathematics.
• At least one published peer-reviewed paper.
• Advanced Python proficiency for scientific coding.
• Ability to design research-grade or graduate-level computational mathematics problems.
• Ability to verify mathematical accuracy, reliability, and logical coherence.
• Strong analytical reasoning and problem-solving ability.
• Clear written and verbal communication for technical documentation and collaboration.
• Careful attention to correctness and the ability to assess difficulty and problem structure.

Helpful Background

Experience developing research-grade mathematical problems, explaining computational reasoning, or reviewing technical solutions can help you contribute effectively. Constructive review judgment is particularly relevant because the role includes evaluating both problems and their solutions.

• Research-grade mathematical problem development
• Clear explanations of computational reasoning
• Technical solution review
• Structured evaluation of problem difficulty and content

Build Your AI Training Career With OpenTrain

AI training is a fast-growing area of technical work where specialists directly influence how advanced models learn and behave. OpenTrain gives you a place to build a profile around your experience, discover projects aligned with your skills, and grow your work into a durable AI training portfolio.

Apply through OpenTrain to bring your computational mathematics expertise to challenging AI evaluation and problem-design work.