Scientific Computing / Research Engineering Expert — Engineering

• Design realistic, multi-step terminal tasks based on real-world engineering and scientific workflows.
• Create engineering datasets, simulation inputs, geometry files, sensor data, design constraints, and configuration files.
• Develop expert solutions using Python, C/C++, Julia, MATLAB/Octave, Bash, or relevant engineering software.
• Build reproducible, containerized environments with appropriate engineering tools and pinned dependencies.
• Develop tasks involving simulation, numerical analysis, optimization, control systems, signal processing, finite-element concepts, CAD-related data, and engineering design.
• Create automated tests and objective grading criteria that validate engineering correctness, including units, physical constraints, tolerances, convergence, stability, boundary conditions, and numerical behavior.
• Debug solver, dependency, workflow, precision, and performance issues and clearly document assumptions, requirements, expected outputs, and edge cases.

• Ph.D., postdoctoral experience, or equivalent advanced technical experience in an Engineering discipline such as mechanical, electrical, chemical, aerospace, civil, materials, biomedical, robotics, or control systems.
• Strong scientific programming skills in Python, C/C++, Julia, MATLAB/Octave, Bash, or another relevant language.
• Hands-on experience working in Linux or terminal-based environments.
• Experience with engineering simulation, modeling, numerical analysis, optimization, signal processing, control systems, or technical data analysis.
• Strong understanding of numerical methods, engineering units, physical constraints, boundary conditions, and technical validation.
• Ability to build, debug, and validate reproducible computational engineering workflows.

• Experience with scientific libraries such as NumPy, SciPy, pandas, matplotlib, SymPy, or PyTorch.
• Familiarity with engineering tools such as OpenFOAM, CalculiX, FEniCS, ROS, LTspice-compatible workflows, QEMU, or similar software.
• Experience with finite-element methods (FEM), computational fluid dynamics (CFD), robotics, embedded systems, control systems, or digital twins.
• Familiarity with Docker, Git, CI/CD, automated testing, or HPC environments.
• Experience developing technical benchmarks, programming tasks, simulation-based evaluations, or automated graders.
• Experience evaluating AI coding or terminal agents or working in research software engineering.
• Publications, patents, open-source contributions, or industry experience involving computational engineering.

Project and contracting process: the hiring partner. Applications continue on the provider's website.