You will lead and mentor a pod of about 5 to 10 technical trainers who create terminal-based engineering tasks for AI training and evaluation. Your work will ensure that tasks reflect real engineering workflows, produce repeatable results, and require meaningful technical reasoning.
You will review technical files, simulation setups, reference solutions, automated tests, and computing environments. You will also give actionable feedback, identify errors or weak assumptions, and track revisions.
- Review problem statements, geometry and mesh files, simulation inputs, material properties, reference solutions, automated tests, and computational environments.
- Validate work 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 automated graders that measure stresses, deflections, natural frequencies, aerodynamic coefficients, thermal performance, orbital parameters, and safety margins.
- Identify inaccurate assumptions, technical errors, flawed tolerances, and ways to bypass genuine engineering calculations.
- Lead and mentor approximately 5 to 10 technical trainers while supporting quality, productivity, and delivery.
What it pays and takes
This is a remote contractor assignment lasting approximately four weeks. The listing does not provide a pay rate.
The role requires advanced technical experience in mechanical engineering, aerospace engineering, or a closely related field, plus strong programming and Linux skills. You should have hands-on experience in at least one listed engineering analysis area and experience reviewing complex technical work or leading small technical teams.
- Pay: The listing does not specify a rate.
- Time: 20+ hours per week, including at least four hours of daily commitment and four hours of overlap with Pacific Time.
- Location: Remote and open worldwide.
- Language: Fluent in English.
- Experience level: Entry level.
- Education or experience: Ph.D., postdoctoral experience, or equivalent advanced technical experience in mechanical engineering, aerospace engineering, or a closely related discipline.
- Programming: Strong skills in Python, C, C++, Fortran, Julia, MATLAB, or Octave, plus Linux proficiency.
- Technical background: Hands-on expertise in finite-element analysis, computational fluid dynamics, dynamics, flight simulation, thermal modeling, astrodynamics, guidance, navigation and control, or propulsion modeling.
- Knowledge: Engineering simulation, numerical methods, computational modeling, technical validation, and solver convergence.
- Leadership: Experience reviewing complex technical work and mentoring or leading small technical teams.
- Helpful tools: OpenFOAM, SU2, CalculiX, FEniCS, Gmsh, FreeCAD, MuJoCo, ROS, OpenVSP, OpenMDAO, ANSYS, Abaqus, or NASTRAN.
- Helpful software practices: 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 uses examples, reviews, and technical evaluations prepared by people to improve how artificial intelligence systems work. Specialists are needed to check whether complex engineering tasks, calculations, and model outputs are accurate and meaningful.
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