Scientific Computing Expert — Mathematics PHD/Approved Task
Listed on 2026-10-30
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Research/Development
Mathematics
Design and build computational mathematics tasks for AI evaluation.
About TuringTuring is one of the world's leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent.
Engagement DetailsCommitments
Required:
At least 6 hours per day and a minimum of 40 hours per week, with a 4-hour overlap with PST.Employment Type:
Contractor assignment (no medical/paid leave)Duration of
Contract:
5 weeks (expected start date is next week)Payment Type:
Pay per task — $300 per approved task
We are seeking Mathematics experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science. You will design tasks involving numerical analysis, optimization, statistics, mathematical modeling, probability, dynamical systems, and computational mathematics. The role focuses on evaluating whether AI agents can formulate mathematical problems, implement reliable algorithms, operate in a terminal environment, debug numerical workflows, and produce verifiable computational results.
What You'll DoDesign and build authentic computational mathematics tasks — translate mathematical and research workflows into self-contained, multi-step terminal environments, complete with datasets, equations, model definitions, constraints, and expected outputs.
Implement expert solutions using Python, R, Julia, C/C++, Bash, or other relevant tools, covering areas such as optimization, numerical integration, differential equations, matrix computation, statistical inference, stochastic modeling, or algorithm analysis.
Define rigorous grading criteria, including numerical accuracy, convergence, complexity, feasibility, and mathematical correctness, along with appropriate tolerances, stopping criteria, stability requirements, and reproducibility controls.
Develop automated tests and debug issues — validate results across edge cases and alternative valid implementations, and troubleshoot problems involving floating-point precision, solver failures, conditioning, convergence, and performance.
Document the work thoroughly, including assumptions, mathematical formulations, expected outputs, and known limitations.
Ph.D., postdoctoral experience, or equivalent advanced technical experience in mathematics, statistics, or a closely related discipline.
Strong programming skills in Python, R, Julia, C/C++, Bash, or another relevant language.
Experience working in Linux or terminal-based environments.
Experience with numerical methods, optimization, statistics, mathematical modeling, or scientific computation.
Ability to independently implement, test, and validate computational algorithms.
Skills: PhD Maths
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