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YO IT CONSULTING

Mathematics Specialist

Posted on Feb. 23, 2026

Remote, United States of America
No Salary information.

Contract

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Contract

Remote

Role Description

Fast-growing AI data services company and subsidiary of, is seeking mathematicians to join our AI training project. In this role, you will review AI-generated responses, evaluate the reasoning quality and step-by-step problem-solving, and provide expert feedback that helps models produce answers that are accurate, logical, and clearly explained. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in calculations, methodology, or conceptual understanding; fact-check quantitative information across; write high-quality mathematical explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on mathematical correctness and reasoning quality.

Your Profile

You hold a MS or PhD in Math/Stats or a related field in one of the top 100 universities.
Confident in pure and applied mathematics, proofs, modeling, statistics/probability, and optimization.
Expert at fact-checking information using public sources across multiple domains.
You communicate mathematics clearly and turn advanced theory into real-world solutions.
Background in research, analytical writing, debate, programming, or mathematics is preferred.
English proficiency C1 and above with excellent writing skills.
For PhD holderss: you have developed or critically reviewed complex math content (e.g., problem banks, proofs, textbook sections, research notes).
Bonus: Data labeling, RLHF, or AI model evaluation experience.

Key Responsibilities

Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.

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