Mathematics Expert - Remote Scientific Computing
Listing checked October 1, 2026 · pay as published by Turing
Overview
Turing wants mathematics experts to build authentic, terminal-based tasks for Terminal Bench Science, testing whether AI agents can handle real computational mathematics. You will craft problems across numerical analysis, optimization, statistics, probability, dynamical systems, and mathematical modeling, then implement expert solutions in Python, R, Julia, C/C++, or Bash. The work centers on translating research workflows into self-contained terminal environments with rigorous grading criteria and reproducible outputs. Every task you design must have verifiable results, clear tolerances, and automated tests that catch edge cases. This is a remote role for someone who combines deep mathematical training with hands-on scientific computing.
What You'll Do10
- 1Design multi-step computational mathematics tasks that mirror authentic research workflows.
- 2Convert mathematical and research processes into self-contained terminal environments.
- 3Prepare datasets, equations, model definitions, constraints, initial conditions, and expected outputs for each task.
- 4Write expert reference solutions using Python, R, Julia, C/C++, Bash, or other relevant tools.
- 5Build tasks around optimization, numerical integration, differential equations, matrix computation, statistical inference, stochastic modeling, or algorithm analysis.
- 6Define grading criteria based on numerical accuracy, convergence, complexity, feasibility, and mathematical correctness.
- 7Set tolerances, stopping criteria, stability requirements, and reproducibility controls for each problem.
- 8Develop automated tests that validate results across edge cases and alternative valid implementations.
- 9Debug floating-point precision, solver failures, conditioning, convergence, and performance issues.
- 10Document assumptions, mathematical formulations, expected outputs, and known limitations.
Requirements6
- 1Ph.D., postdoctoral experience, or equivalent advanced technical background in mathematics, statistics, or a closely related field.
- 2Strong programming ability in Python, R, Julia, C/C++, Bash, or another relevant language.
- 3Comfort working in Linux or terminal-based environments.
- 4Experience with numerical methods, optimization, statistics, mathematical modeling, or scientific computation.
- 5Ability to implement, test, and validate computational algorithms independently.
- 6Solid grasp of numerical stability, error analysis, mathematical assumptions, and reproducibility.
Who Should Apply
This role suits mathematicians, statisticians, and research software engineers who hold a Ph.D. or postdoctoral experience and write production-quality numerical code. The strongest candidates have built solvers or simulations in Python, Julia, C++, or R and can explain why a method converges, what tolerances make sense, and where it breaks. You should apply only if you are comfortable debugging floating-point precision, solver failures, and conditioning issues inside a Linux terminal. Candidates who rely on high-level libraries without understanding the underlying math, or who have never written automated tests for numerical code, tend to score low on fit. A common rejection reason is submitting a task with ambiguous grading criteria or a reference solution that passes only one implementation path.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$80/hour
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Pay not disclosed
Most Machine Learning & AI roles pay $55–$104 per hour.
Based on 518 similar roles that publish pay · 101 publish only a top rate; those count at the rate they gave
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Compensation
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Required Skills
Application Tip
When applying, include a link to a repository or portfolio piece that shows a complete computational mathematics workflow: problem statement, reference solution in Python or Julia, tolerance choices, and automated tests. Quantify the numerical accuracy or convergence behavior you achieved. Naming specific libraries such as NumPy, SciPy, JAX, or CVXPY and a solver like IPOPT or Gurobi helps your fit score because the task-design work leans on those exact tools.
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