Remote Pod Lead - Math

Turing, a rapidly growing leader in AI innovation, is seeking an experienced Mathematics expert to lead a team of 5–10 trainers in developing realistic, terminal-based scientific and technical tasks for Terminal Bench Science. This role focuses on creating and reviewing tasks that reflect authentic mathematical research and computational workflows, ensuring mathematical rigor and reproducibility across numerical and applied mathematics, scientific computing, optimization, statistics, mathematical modeling, computational algebra, and formal verification.

Key Responsibilities:
- Review tasks comprehensively, including problem statements, datasets, environments, reference solutions, and automated tests.
- Verify mathematical correctness, well-posedness, convergence, stability, conditioning, discretization error, and edge-case handling.
- Ensure tasks require genuine multi-step reasoning rather than simple lookups or single library calls.
- Validate appropriate use of exact, symbolic, arbitrary-precision, or floating-point computations.
- Confirm tolerances align with the problem’s numerical conditioning.
- Design and review graders based on mathematical properties such as residuals, feasibility, optimality certificates, invariants, error bounds, or proof checking.
- Guarantee task reproducibility through pinned tools, libraries, solver versions, and dependencies.
- Detect and prevent hard-coded solutions, test-file leakage, or other bypass methods.
- Provide precise, actionable feedback and track trainer revisions until closure.
- Mentor and onboard trainers on mathematical rigor, task design, coding practices, and evaluation standards.
- Allocate work, monitor quality and throughput, and escalate blockers as needed.
- Maintain pod documentation covering guidelines, recurring issues, and exemplary tasks.
- Collaborate with the Mathematics Team Lead and other stakeholders to enhance benchmark quality.

Required Qualifications:
- Ph.D., postdoctoral experience, or equivalent advanced expertise in mathematics, applied mathematics, statistics, or a related field.
- Strong programming skills in Python, Julia, C/C++, MATLAB/Octave, or computer algebra systems.
- Proficiency with Linux and terminal environments.
- Hands-on experience with numerical methods, scientific computing, symbolic computation, optimization, computational statistics, or mathematical modeling.
- Ability to rigorously evaluate mathematical derivations, algorithms, proofs, and computational results.
- Experience reviewing research outputs, code, mathematical work, or scientific workflows.
- Proven experience mentoring or leading small technical or research teams.
- Strong written communication skills with the ability to provide precise, constructive feedback.
- Capability to assess reproducibility, test reliability, and scientific validity of computational tasks.

Preferred Qualifications and Benefits:
- Familiarity with libraries and tools such as NumPy, SciPy, SymPy, mpmath, SageMath, PARI/GP, GAP, Macaulay2, or Mathematica-like workflows.
- Experience with ODE/PDE tools like FEniCS, Firedrake, DifferentialEquations.jl, PETSc.
- Knowledge of optimization frameworks such as CVXPY, JuMP, Pyomo, HiGHS.
- Exposure to proof assistants including Lean/mathlib, Coq/Rocq, Isabelle.
- Experience with Docker, Git, continuous integration systems, and automated testing.
- Background in designing benchmarks, competition problems, or computational evaluations.
- Experience evaluating AI coding, reasoning, or terminal agents.
- Involvement in setting or judging mathematics olympiad or competition problems.
- Publications or open-source contributions in computational mathematics, numerical software, or formalized mathematics.
- Prior experience in AI training, data generation, or model evaluation programs.

Offer Details:
This is a contractor assignment requiring a minimum commitment of 40 hours per week, with at least 4 hours daily and a 4-hour overlap with Pacific Standard Time (PST). The contract duration is four weeks, with an expected start date as early as next week. Please note that this role does not include medical benefits or paid leave.