Mathematics AI Training Expert – PhD

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Anyone AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

📚 Education

🤖 Artificial Intelligence

🎯 Recruiter

💰 $1.5M Pre Seed Round - Anyone AI on 2022-06

Education • Artificial Intelligence • Recruitment

Anyone AI is a Latin America–focused career accelerator and training academy that develops software engineers into AI and machine learning professionals through intensive, remote, mentor-led programs. It combines technical coursework (e. g. , Machine Learning Developer track), professional preparation (resume/LinkedIn optimization, interview coaching, English practice), and a placement-oriented model (income-share payments and access to employer partnerships) to help graduates secure higher-paying AI roles globally. Anyone AI also operates a talent marketplace connecting its community to companies seeking AI talent.

📋 Description

• Design advanced mathematical problems for frontier AI training and evaluation • Create deterministic problems with exactly one correct answer • Write complete, verified solutions and clearly document the reasoning process • Develop problems that test deep mathematical reasoning, not just memorization • Where relevant, use Python or specialized tools to build computational workflows • Ensure all outputs are technically precise, reproducible, and well-written in English

🎯 Requirements

• Master’s or PhD in Mathematics or a closely related field • Strong research or industry experience involving mathematical modeling, proof-based reasoning, applied mathematics, or computational mathematics • Strong Python skills; comfort with libraries such as numpy, scipy, pandas, or similar • Solid grasp of algorithms, numerical methods, and computational approaches • Ability to design original, difficult problems that mirror real mathematical workflows • Excellent attention to detail and technical writing skills in English • Experience with symbolic math systems, theorem provers, optimization solvers, or other mathematical software (nice to have) • Background in olympiad-level, graduate-level, or research-level problem design (nice to have) • Experience evaluating model reasoning, benchmarking, or technical assessment design (nice to have)