Physical Sciences Expert
We are seeking Physical Sciences experts to develop realistic, terminal-based scientific tasks for Terminal Bench Science.
Hiring companyTuring
About the role
You will translate authentic physics, chemistry, materials science, astronomy, and computational science workflows into reproducible benchmark environments.
The role involves creating scientific inputs, computational models, executable solutions, automated tests, and objective grading criteria. You will help evaluate whether AI agents can reason through scientific problems, operate command-line tools, debug calculations, and produce reliable scientific artifacts.
Core Domains
Physics, chemistry, materials science, computational physics, computational chemistry, astronomy, thermodynamics, quantum mechanics, statistical mechanics, or materials modeling.
Responsibilities
- Design multi-step terminal tasks based on realistic physical-science workflows.
- Develop self-contained computational environments with pinned dependencies and scientific software.
- Create input datasets, molecular structures, simulation parameters, experimental data, or model configurations.
- Implement expert solutions using Python, Bash, C/C++, Julia, or domain-specific tools.
- Develop automated tests for numerical accuracy, physical consistency, convergence, and output structure.
- Create tasks involving simulations, numerical modeling, data fitting, optimization, spectroscopy, molecular analysis, or scientific visualization.
- Define appropriate numerical tolerances, units, boundary conditions, and expected scientific behavior.
- Validate that tasks are reproducible and execute successfully without runtime downloads.
- Debug issues involving dependencies, precision, solver stability, performance, and file formats.
- Communicate scientific assumptions and computational limitations clearly to reviewers.
Required skills
- Strong programming skills in Python, C/C++, Julia, Bash, or another scientific programming language.
- Experience working in Linux or terminal-based environments.
- Experience with numerical methods, scientific modeling, simulations, or quantitative data analysis.
- Ability to create and validate computational scientific workflows independently.
- Strong understanding of units, numerical precision, physical constraints, and scientific reproducibility.
Preferred skills
- Experience with NumPy, SciPy, pandas, matplotlib, SymPy, JAX, or similar libraries.
- Familiarity with molecular dynamics, quantum chemistry, finite-difference methods, Monte Carlo methods, optimization, or statistical modeling.
- Experience with tools such as OpenMM, ASE, RDKit, Psi4, LAMMPS, GROMACS, or other domain-specific software.
- Familiarity with Docker, Conda, Git, CI systems, and automated testing.
- Experience working with HPC systems or performance-sensitive scientific workloads.
- Experience evaluating AI coding or terminal agents.
- Research software engineering experience.
- Experience creating benchmark tasks or automated graders.
- Publications or open-source contributions involving computational science.
- Experience converting experimental or research workflows into reproducible packages.
Experience and education
- Ph.D., postdoctoral experience, or equivalent advanced technical experience in a relevant physical-science discipline.
Eligibility
Work arrangement: Fully remote
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