Computational Mathematics AI Problem Author

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. It connects specialists with projects where human expertise helps develop better artificial intelligence, while giving contributors a place to build a lasting professional profile.

Creating an OpenTrain account is free, and candidates can apply in minutes. Your profile can help showcase relevant experience as you grow in the expanding AI training industry.

About AI Training and Data Labeling

AI training is the human side of building modern artificial intelligence. Specialists create, review, and evaluate examples that help AI systems reason more accurately, follow instructions, and perform technical tasks.

This role focuses on advanced mathematical problem creation and evaluation. Your research judgment and scientific programming skills will help produce challenging, reliable training material for AI research workflows.

The Role

OpenTrain is recruiting a Computational Mathematics AI Problem Author and Reviewer to create and solve advanced scientific problems used in AI research and training workflows. You will combine mathematical research judgment with Python-based scientific programming to develop challenging, well-defined problems and dependable solution paths.

The role emphasizes graduate-level quality, structured difficulty tagging, rigorous two-tier review, and clear technical reasoning. It is a remote contractor assignment lasting three months, with possible extension based on performance and project needs.

• Contractor and part-time assignment
• Three-month duration, with potential extension
• Remote and worldwide
• Commitment options of 20, 30, or 40 hours per week
• Minimum commitment of 20 hours per week
• At least four hours per day
• Four hours of overlap with Pacific Standard Time
• English-language work

What You'll Do

You will turn complex computational mathematics concepts into precise problems that can be used in AI training and research settings. You will also construct computational solutions, evaluate submissions, and document decisions so that each contribution is understandable and technically dependable.

• Develop original research-grade or graduate-level scientific problems in computational mathematics.
• Use Python to devise computational solutions and verify their accuracy, reliability, and logical coherence.
• Assign problem categories, secondary tags, and appropriate difficulty levels.
• Participate in two-tier reviews of problems and solutions.
• Identify accuracy, quality, or coherence issues.
• Incorporate feedback from reviewers and team leads.
• Document problem statements, solution approaches, and review decisions.
• Communicate technical reasoning clearly in written and verbal discussions.

Required Qualifications

This opportunity requires advanced academic and technical expertise in computational mathematics. Candidates should be able to assess both the mathematical correctness and computational reliability of graduate-level problems and solutions.

• PhD or current PhD candidacy in Computational Mathematics
• At least one published peer-reviewed paper
• Advanced Python programming proficiency for scientific coding
• Ability to formulate graduate-level computational mathematics problems
• Ability to verify mathematical solutions for accuracy and logical coherence
• Strong analytical and logical-thinking skills
• Strong problem-solving skills
• Strong written and verbal communication skills
• Judgment in assigning categories, tags, and difficulty levels

Helpful Research Background

Research experience involving computational mathematics, scientific programming, graduate-level problem formulation, or peer-reviewed technical work is especially relevant. The role suits specialists who can translate difficult research concepts into precise challenges, build dependable solutions, and respond constructively to detailed quality feedback.

• Computational mathematics research
• Scientific programming
• Graduate-level problem formulation
• Peer-reviewed technical research
• Mathematical problem and solution evaluation

Why Build an AI Training Career

AI training and data-labeling work is a growing area of technology where human specialists directly influence how advanced AI systems behave. Contributors use their subject expertise to create examples, evaluate model outputs, and improve the quality of AI-generated reasoning.

OpenTrain helps contributors discover relevant opportunities, manage their professional profile, and build a portfolio of AI training experience. Flexible remote assignments can fit around other commitments while providing hands-on work in a rapidly developing field.

How to Apply

Create a free OpenTrain account, complete your profile with your computational mathematics, publication, and Python experience, and apply through OpenTrain. Be sure to highlight your ability to formulate rigorous problems, verify computational solutions, and evaluate technical quality.