Remote Math Research Pro for AI Training

RemoteJobsOne; Ciudad de México; A distancia; MXN 2.007.000 - 2.258.000; Jornada completa

Descripción de la vacante

RemoteJobsOne is seeking a Mathematics Expert for a fully remote contractor role based in Mexico. You will apply advanced mathematical knowledge to train AI systems, author proofs, and provide rigorous technical analyses.

The position welcomes candidates globally, with emphasis on strong problem-solving and clear communication of complex ideas. The role focuses on developing and refining mathematical content for AI training datasets, with an emphasis on research-grade rigor and collaboration

Formación

• Advanced knowledge of calculus and higher mathematics concepts.
• Strong problem-solving and critical thinking skills.
• Clear written and verbal communication of complex ideas.

Responsabilidades

• Author and review advanced mathematical problems, solutions, and proofs spanning calculus and other higher mathematics domains.
• Design, analyze, and provide rigorous feedback on mathematical challenges, ensuring clarity and accuracy.
• Evaluate and refine mathematical content for use in AI training datasets.
• Contribute technical analyses, explanations, and justifications with research-level rigor.
• Apply research methodology for developing and verifying mathematical concepts.
• Communicate complex mathematical ideas in precise, accessible written and verbal formats for diverse audiences.
• Collaborate with project teams to provide domain expertise to enhance AI learning processes.

Descripción del empleo

RemoteJobsOne is seeking a Mathematics Expert for a fully remote contractor role based in Mexico. You will apply advanced mathematical knowledge to train AI systems, author proofs, and provide rigorous technical analyses.

The position welcomes candidates globally, with emphasis on strong problem-solving and clear communication of complex ideas. The role focuses on developing and refining mathematical content for AI training datasets, with an emphasis on research-grade rigor and collaboration