Pod lead

Hello,

Greetings from ZettaMine!!!

Freelancing Opportunity :- We are hiring a Pod Lead – Math (Terminal Bench Science) for a short-term Frontier AI Research, Mathematical Computing & Scientific Evaluation project across the following locations:

Eligible Locations: 🇧🇩 Bangladesh | 🇧🇷 Brazil | 🇨🇴 Colombia | 🇪🇬 Egypt | 🇬🇭 Ghana | 🇮🇳 India | 🇮🇩 Indonesia | 🇳🇬 Nigeria | 🇹🇷 Turkey

Role: Pod Lead – Math (Terminal Bench Science)

Educational Eligibility: PhD, postdoctoral experience, or equivalent advanced technical experience in Mathematics, Applied Mathematics, Statistics, or a closely related discipline.

Mode: Remote

Start: Immediate

Engagement Type: Short-Term Contract

Mandatory Eligibility Criteria:

Technical Qualification:

• PhD, postdoctoral experience, or equivalent advanced expertise in Mathematics, Applied Mathematics, Statistics, or a closely related field.
• Strong programming skills in Python, Julia, C/C++, MATLAB/Octave, or computer algebra systems.
• Strong proficiency in Linux environments and command-line/terminal workflows.
• Hands-on expertise in at least one major area: Numerical Methods | Scientific Computing | Symbolic Computation | Optimization | Computational Statistics | Mathematical Modeling.
• Ability to rigorously evaluate mathematical derivations, algorithms, proofs, numerical methods, and computational results.
• Strong understanding of mathematical correctness, convergence, stability, conditioning, discretization errors, numerical precision, and edge-case handling.
• Experience reviewing mathematical research, scientific code, computational workflows, or technical deliverables.
• Ability to assess computational accuracy, reproducibility, test reliability, and the scientific validity of mathematical tasks.
• Experience mentoring technical or research teams, reviewing deliverables, and maintaining quality standards.
• Excellent written communication skills to provide precise technical feedback and document mathematical requirements.

Availability:

• Minimum 40 hours per week.
• Availability to work at least 8 hours per day.
• Minimum 4 hours of overlap with PST business hours.

What You’ll Work On:

• Lead and mentor a pod of approximately 5–10 technical trainers developing realistic, terminal-based mathematical and scientific tasks for a frontier AI training and evaluation initiative.
• Review mathematical tasks end to end, including problem statements, datasets, computational environments, reference solutions, and automated tests.
• Validate tasks involving numerical and applied mathematics, scientific computing, optimization, statistics, mathematical modeling, computational algebra, and formal verification.
• Ensure tasks reflect authentic mathematical research and computational workflows that require meaningful, multi-step reasoning rather than simple lookups or single-library calls.
• Verify mathematical correctness, problem formulation, well-posedness, convergence, stability, conditioning, discretization errors, and edge-case handling.
• Validate the appropriate use of exact, symbolic, arbitrary-precision, and floating-point computations.
• Ensure numerical tolerances are appropriate for the conditioning and requirements of each problem.
• Design and review automated graders using mathematical properties such as residuals, feasibility, optimality certificates, invariants, error bounds, and proof checking.
• Ensure tasks and computational results are reproducible through pinned tools, libraries, solver versions, and dependencies.
• Identify and prevent hard-coded solutions, test-data leakage, and other approaches that bypass the intended mathematical computation.
• Review reference solutions and automated tests to ensure they accurately assess mathematical reasoning, computational correctness, and scientific validity.
• Identify mathematical inaccuracies, flawed assumptions, unreliable tests, and opportunities to improve task quality and evaluation standards.
• Provide actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
• Onboard and mentor trainers on mathematical rigor, task design, coding practices, and evaluation standards.
• Allocate work, monitor quality and productivity, and resolve technical blockers.
• Maintain technical documentation covering task guidelines, recurring issues, quality standards, and exemplary tasks.
• Collaborate with the Mathematics Team Lead and other technical stakeholders to continuously improve benchmark quality.

Nice to Have:

• Experience with NumPy, SciPy, SymPy, mpmath, SageMath, PARI/GP, GAP, Macaulay2, or Mathematica-like workflows.
• Experience with FEniCS, Firedrake, DifferentialEquations.jl, PETSc, or similar numerical ODE/PDE tools.
• Familiarity with optimization frameworks such as CVXPY, JuMP, Pyomo, or HiGHS.
• Familiarity with formal proof assistants such as Lean/mathlib, Coq/Rocq, or Isabelle.
• Experience with Docker, Git, CI systems, and automated testing.
• Experie