Pod lead - Math (Terminal Bench Science)

About Turing: Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies. Role Overview We are seeking an experienced Mathematics expert to lead a pod of 5–10 trainers developing realistic, terminal-based scientific and technical tasks for Terminal Bench Science. You will review trainer-created tasks for mathematical correctness, reproducibility, challenge, and grader reliability. You will also mentor trainers, manage quality and throughput, and ensure that tasks reflect authentic mathematical research and computational workflows. The role covers numerical and applied mathematics, scientific computing, optimization, statistics, mathematical modeling, computational algebra, and formal verification. What you’ll do Review tasks end to end, 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 lookup or a single library call. Validate that exact, symbolic, arbitrary-precision, or floating-point computation is used appropriately. Check that tolerances are suitable for 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. Ensure tasks are reproducible through pinned tools, libraries, solver versions, and dependencies. Identify and prevent hard-coded solutions, test-file leakage, and other methods of bypassing the intended computation. Provide precise, actionable feedback and track trainer revisions through closure. Mentor and onboard trainers on mathematical rigor, task design, coding practices, and evaluation standards. Allocate work, monitor quality and throughput, and escalate blockers. Maintain pod documentation covering guidelines, recurring issues, and exemplary tasks. Collaborate with the Mathematics Team Lead and other technical stakeholders to improve benchmark quality. What we’re looking for Ph.D., postdoctoral experience, or equivalent advanced experience in mathematics, applied mathematics, statistics, or a related field. Strong programming skills in Python, Julia, C/C++, MATLAB/Octave, or a computer algebra system. Strong Linux and terminal proficiency. 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. Experience mentoring or leading small technical or research teams. Strong written communication and the ability to provide precise, constructive feedback. Ability to assess reproducibility, test reliability, and the scientific validity of computational tasks. 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 ODE/PDE tools. Experience with CVXPY, JuMP, Pyomo, HiGHS, or other optimization frameworks. Familiarity with Lean/mathlib, Coq/Rocq, Isabelle, or other proof assistants. Experience with Docker, Git, CI systems, and automated testing. Experience designing benchmarks, competition problems, or computational evaluations. Experience evaluating AI coding, reasoning, or terminal agents. Experience 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: Commitments Required: At least 4 hours per day and minimum 40 hours per week with overlap of 4 hours with PST. Employment type : Contractor assignment (no medical/paid leave) Duration of contract : 4 week [expected start date is next week]