Remote Pod Lead

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]