AI Trainer; Mathematics & Statistics

Position: AI Trainer (Mathematics & Statistics)
Open role at a frontier AI lab# AI Trainer (Mathematics & Statistics)
Frontier AI Lab
· Remote
· Full-time## Ready to see if you fit?

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Sign up to check your fit## About the role

Pay: $60–$120/hour equivalent, paid per accepted task

Location:

Global, fully remote

Job Type:
Contractor Time Commitment: 15+ hours per week

Schedule:

Flexible Our team is looking for Computational Mathematicians, Statisticians, and Optimization Experts with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems.

You will use your mathematical expertise and programming skills to create challenging technical tasks, evaluate model-generated code and reasoning, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have completed substantive AI-training work involving mathematical, statistical, or optimization content.

What You’ll Work On
• Create realistic computational mathematics and statistics tasks that challenge advanced AI models.
• Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate model-generated proofs, calculations, code, models, optimizations, and technical deliverables.
• Identify logical errors, invalid assumptions, numerical instability, incorrect statistical methods, or reproducibility failures.

Required Qualifications
• Prior paid experience training, evaluating, benchmarking, or improving AI systems on mathematical or technical tasks.
• Strong practical proficiency with Python.
• Deep expertise in at least one relevant mathematical specialization.
• Hands-on experience using computational methods for numerical analysis, optimization, statistics, theorem proving, modeling, or scientific computing.

Relevant specializations may include applied mathematics, scientific computing, operations research, optimization, formal mathematics, theorem proving, probability, statistics, numerical methods, and mathematical modeling.

Public Proof of Skill Strong applicants should provide evidence such as:
• Peer-reviewed publications or preprints
• Public mathematical, statistical, or optimization code
• Google Scholar, ORCID, arXiv, or equivalent research profiles
• Open-source numerical or mathematical software contributions
• Competition, theorem-proving, or optimization results
• Reproducible notebooks, models, proofs, benchmarks, or technical projects###

Preferred Qualifications
• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience reviewing other AI trainers’ technical submissions.
• A master’s degree, PhD, or equivalent record of substantial computational work.
• Experience with Num Py, Sci Py, Sym Py, pandas, Pyomo, OR-Tools, Lean, Coq, or comparable tools.
• Experience validating numerical solutions, statistical results, optimization outputs, or formal proofs.
• Availability to begin immediately. Application Process
• Submit your application, AI-training experience, specialization, and public technical evidence.
• Complete an AI interview containing technical and integrity checks.
• Complete a hiring-manager review.
• If selected, complete project onboarding and begin your first assigned task.### Compensation
• Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.