Senior AI Video Trainer (US Based) - Remote

Job Description: Computational Mathematician, Statistician & Optimization Expert – AI Training & Model Evaluation

Role Type: Contractor Location: Global (Fully Remote) Time Commitment: 15+ hours per week Schedule: Flexible

About the Role

We are seeking experienced Computational Mathematicians, Statisticians, and Optimization Experts with prior professional AI-training or model-evaluation experience to contribute to a project focused on improving advanced AI systems.

You will apply your mathematical expertise and programming skills to develop challenging technical tasks, evaluate AI-generated code and reasoning, identify errors, and establish objective standards for accurate solutions. This opportunity is intended for professionals with substantive AI-training experience involving mathematical, statistical, or optimization content.

Key Responsibilities

• Create realistic computational mathematics and statistics tasks that challenge advanced AI models.
• Develop clear task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate AI-generated proofs, calculations, code, models, optimizations, and technical deliverables.
• Identify logical errors, invalid assumptions, numerical instability, incorrect statistical methods, and reproducibility failures.
• Assess the mathematical accuracy, reliability, and completeness of model-generated outputs.
• Establish objective standards for evaluating computational and mathematical solutions.

Required Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on mathematical or technical tasks.
• Strong practical proficiency in 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

• Applied Mathematics
• Scientific Computing
• Operations Research
• Optimization
• Formal Mathematics
• Theorem Proving
• Probability and Statistics
• Numerical Methods
• Mathematical Modeling

Public Proof of Technical Expertise

Strong applicants should be able to demonstrate their expertise through 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, evaluation rubrics, graders, or benchmark problems.
• Experience reviewing technical submissions from other AI trainers.
• A master's degree, PhD, or equivalent record of substantial computational work.
• Familiarity with NumPy, SciPy, SymPy, 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

- Application Submission: Submit your application along with details of your AI-training experience, mathematical specialization, and public technical evidence.

- AI Interview: Complete an AI interview that includes technical and integrity checks.

- Hiring Manager Review: The team evaluates your profile and interview performance.

- Onboarding: Selected candidates complete project onboarding and begin their first assigned task.

Compensation

Compensation is output-based, with payment for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.

Originally posted on Himalayas