Pod lead - Math (Terminal Bench Science)

Zettamine Labs; United States; À distance; USD 55 000 - 109 000; Temps partiel

Résumé du poste

Zettamine Labs seeks a POD Lead Mathematics Expert for a short-term Frontier AI Research initiative. You will lead a small team of technical trainers, review complex mathematical tasks, datasets, and evaluation environments in a remote contractor setting.

You will validate numerical methods, proofs, and benchmarks, ensuring rigorous multi-step reasoning, reproducibility, and high-quality documentation within a 3-week engagement.

Qualifications

• PhD/postdoc or equivalent advanced research experience in Mathematics or related field.
• Strong programming skills in Python, Julia, C/C++, MATLAB/Octave or CAS.
• Solid Linux/terminal proficiency and scripting.
• Experience reviewing mathematical derivations, computational models, or scientific code.

Responsabilités

• Lead and mentor a pod of technical trainers (~5-10 people).
• Review tasks end-to-end including problem statements, datasets, environments, and tests.
• Validate numerical analysis, optimization, statistics, differential equations, and modeling tasks.

Connaissances

Python

Julia

C/C++

MATLAB/Octave

Linux/terminal

Numerical methods

Mathematical proofs

Academic review

Communication skills

Formation

PhD or postdoc experience

Outils

Docker

Git

CI systems

Automated testing

Description du poste

Hello,

Greetings from ZettaMine!!!

Freelancing Opportunity

We are hiring a POD Lead Mathematics Expert (Terminal Bench Science) for a short-term Frontier AI Research, Scientific Computing & AI Model Evaluation initiative.

Location

India

Role

POD Lead Mathematics Expert (Terminal Bench Science)

Educational Eligibility

PhD, postdoctoral experience, or equivalent advanced research experience in Mathematics, Applied Mathematics, Statistics, or a related field.

Mode

Remote

Start

Expected next week

Duration

3 Weeks

Employment Type

Contractor assignment (no medical or paid leave).

Mandatory Eligibility Criteria

Technical Qualification

• Strong programming skills in Python, Julia, C/C++, MATLAB/Octave, or computer algebra systems.
• Strong proficiency in Linux and terminal environments.
• Hands-on experience in Numerical Methods, Scientific Computing, Optimization, Symbolic Computation, Computational Statistics, or Mathematical Modeling.
• Strong understanding of mathematical proofs, algorithms, convergence, stability, numerical conditioning, and error analysis.
• Experience reviewing mathematical derivations, research outputs, computational models, or scientific code.
• Ability to validate mathematical correctness, numerical accuracy, solver performance, and reproducibility.
• Experience designing or reviewing automated tests, mathematical benchmarks, or computational evaluations.
• Experience mentoring technical teams, reviewing deliverables, and maintaining quality standards.
• Excellent written communication skills for providing precise technical feedback and documentation.

Preferred Qualifications

• Familiarity with NumPy, SciPy, SymPy, mpmath, SageMath, PARI/GP, or Mathematica.
• Experience with FEniCS, Firedrake, PETSc, DifferentialEquations.jl, CVXPY, JuMP, or Pyomo.
• Familiarity with Lean/mathlib, Coq/Rocq, Isabelle, or other proof assistants.
• Experience with Docker, Git, CI systems, and automated testing.
• Experience designing mathematical benchmarks, competition problems, or simulation-based evaluations.
• Prior experience evaluating AI coding agents, mathematical reasoning, or AI training programs.

What You'll Work On

• Lead and mentor a pod of approximately 510 technical trainers developing realistic, terminal-based mathematical tasks for a frontier AI training and evaluation initiative.
• Review tasks end to end, including problem statements, datasets, computational environments, reference solutions, and automated tests.
• Validate tasks involving numerical analysis, optimization, statistics, differential equations, mathematical modeling, computational algebra, and formal verification.
• Ensure tasks require genuine multi-step reasoning rather than simple lookups or single-library calls.
• Verify convergence, stability, numerical conditioning, discretization errors, and appropriate computational precision.
• Review automated graders to ensure they evaluate mathematical properties, residuals, feasibility, optimality, error bounds, and proof correctness.
• Identify mathematical inaccuracies, unrealistic assumptions, hard-coded outputs, and opportunities to bypass intended computations.
• Provide actionable feedback, track revisions, and ensure quality issues are resolved before task approval.
• Onboard and mentor trainers, allocate work, monitor quality and productivity, and resolve technical blockers.
• Maintain technical documentation, task design guidelines, and best practices.

Availability Requirements

• Minimum 40 hours per week.
• At least 4 hours of availability per day.
• Minimum 4 hours of overlap with PST business hours.