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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