Scientific Computing / Research Engineering Expert — Engineering

Role Overview:

We are staffing a frontier AI initiative that requires strong Engineering experts to develop realistic, terminal-based scientific and technical tasks used to train and evaluate AI agents.

The work involves translating authentic engineering workflows into reproducible computational tasks that test modeling, simulation, optimization, data processing, debugging, and technical validation. The role spans disciplines including mechanical, electrical, chemical, aerospace, civil, materials, biomedical, robotics, and control systems engineering.

What you'll do:

• 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.

What we're looking for:

• 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.

Nice to have:

• 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.