The work
You will lead and mentor a pod of about 5 to 10 technical trainers who create realistic, terminal-based engineering tasks for AI training and evaluation. Your work will focus on technical accuracy, reproducible results, meaningful engineering reasoning, and consistent delivery.
You will review engineering problem statements, geometry and mesh files, simulation inputs, material properties, reference solutions, automated tests, and computational environments. You will also check automated graders and give clear feedback so trainers can correct errors and improve their work.
- Review tasks involving finite-element analysis, computational fluid dynamics, thermodynamics, heat transfer, dynamics, robotics, flight simulation, propulsion, and orbital mechanics.
- Check units, equilibrium, conservation laws, boundary conditions, mesh convergence, solver tolerances, reference frames, numerical stability, and reproducibility.
- Review outputs such as stresses, deflections, natural frequencies, aerodynamic coefficients, thermal performance, orbital parameters, and safety margins.
- Find inaccurate assumptions, technical errors, flawed tolerances, and ways to bypass genuine engineering calculations.
- Track revisions and support consistent quality, productivity, and delivery across the pod.
What it pays and takes
This is a remote contractor assignment lasting approximately four weeks. Compensation follows the project's standard terms. The work requires a regular weekly commitment and daily overlap with Pacific Time.
- Pay: Standard project terms apply.
- Schedule: Minimum 40 hours per week, including at least four hours of daily commitment.
- Time zone: At least four hours of overlap with Pacific Time.
- Location: Remote and open worldwide.
- Language: Fluent English.
- Education or experience: Ph.D., postdoctoral experience, or equivalent advanced technical experience in mechanical engineering, aerospace engineering, or a closely related field.
- Programming: Strong skills in Python, C or C++, Fortran, Julia, MATLAB, or Octave, plus Linux proficiency.
- Technical background: Hands-on expertise in at least one of finite-element analysis, computational fluid dynamics, dynamics, flight simulation, thermal modeling, astrodynamics, guidance, navigation and control, or propulsion modeling.
- Additional skills: Strong knowledge of engineering simulation, numerical methods, computational modeling, technical validation, solver convergence, complex technical review, and small-team mentoring or leadership.
- Helpful tools: OpenFOAM, SU2, CalculiX, FEniCS, Gmsh, FreeCAD, MuJoCo, ROS, OpenVSP, OpenMDAO, ANSYS, Abaqus, or NASTRAN.
- Helpful development experience: Docker, Git, continuous integration and delivery, automated testing, or AI coding agents.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training work uses human-created examples, reviews, and evaluations to help artificial intelligence systems produce more useful and reliable results. This project applies experienced engineering judgment to test whether AI-generated technical work is accurate, reproducible, and based on real engineering calculations.