Math Expert (Terminal Bench)

Crossing Hurdles; United States; Remote; USD 110,000 - 160,000; Full time

Job summary

Crossing Hurdles is seeking a PhD-level or equivalent expert in mathematics to design and implement advanced computational tasks. The role focuses on translating workflows into self-contained terminal environments and producing rigorous, reproducible numerical work.

You will prepare datasets, equations, and model definitions, and use Python, R, Julia, C/C++, or Bash to deliver robust solutions across optimization, differential equations, and stochastic modeling.

Qualifications

• Ph.D. or equivalent advanced technical experience in Mathematics, Statistics, or a closely related discipline.
• Strong expertise in applied mathematics, numerical analysis, optimization, statistics, probability, mathematical modeling, differential equations, dynamical systems, operations research, or computational geometry.
• Strong programming skills in Python, R, Julia, C/C++, Bash, or another relevant programming 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.
• Strong understanding of numerical stability, error analysis, mathematical assumptions, and reproducibility.
• Familiarity with NumPy, SciPy, SymPy, pandas, scikit-learn, JAX, PyTorch, CVXPY, or similar tools is preferred.

Responsibilities

• Design authentic, multi-step computational mathematics tasks.
• Translate mathematical and research workflows into self-contained terminal environments.
• Prepare datasets, equations, model definitions, constraints, initial conditions, and expected outputs.
• Implement expert solutions using Python, R, Julia, C/C++, Bash, or other relevant tools.
• Create tasks involving optimization, numerical integration, differential equations, matrix computation, statistical inference, stochastic modeling, and algorithm analysis.
• Define rigorous grading criteria based on numerical accuracy, convergence, complexity, feasibility, and mathematical correctness.
• Establish appropriate tolerances, stopping criteria, stability requirements, and reproducibility controls.

Skills

Python

R

Julia

C/C++

Bash

Education

PhD in Mathematics/Statistics or related field

Tools

NumPy

SciPy

SymPy

pandas

scikit-learn

JAX

PyTorch

CVXPY

Job description

Role & responsibilities

• Design authentic, multi-step computational mathematics tasks.
• Translate mathematical and research workflows into self-contained terminal environments.
• Prepare datasets, equations, model definitions, constraints, initial conditions, and expected outputs.
• Implement expert solutions using Python, R, Julia, C/C++, Bash, or other relevant tools.
• Create tasks involving optimization, numerical integration, differential equations, matrix computation, statistical inference, stochastic modeling, and algorithm analysis.
• Define rigorous grading criteria based on numerical accuracy, convergence, complexity, feasibility, and mathematical correctness.
• Establish appropriate tolerances, stopping criteria, stability requirements, and reproducibility controls.

Preferred candidate profile

• Ph.D., postdoctoral experience, or equivalent advanced technical experience in Mathematics, Statistics, or a closely related discipline.
• Strong expertise in applied mathematics, numerical analysis, optimization, statistics, probability, mathematical modeling, differential equations, dynamical systems, operations research, or computational geometry.
• Strong programming skills in Python, R, Julia, C/C++, Bash, or another relevant programming 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.
• Strong understanding of numerical stability, error analysis, mathematical assumptions, and reproducibility.
• Familiarity with NumPy, SciPy, SymPy, pandas, scikit-learn, JAX, PyTorch, CVXPY, or similar tools is preferred.