Pod Lead, Mechanical & Aerospace Engineering, Terminal Bench

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 help ensure that tasks reflect real engineering workflows, produce repeatable results, and require meaningful technical reasoning.

You will review task statements, geometry and mesh files, simulation inputs, material properties, reference solutions, automated tests, and computational environments. You will also assess technical work across engineering simulation and analysis, then give clear feedback and track revisions.

• Review finite-element analysis, computational fluid dynamics, thermodynamics, heat transfer, dynamics, robotics, flight simulation, propulsion, and orbital mechanics tasks.
• Check units, equilibrium, conservation laws, boundary conditions, mesh convergence, solver tolerances, reference frames, numerical stability, and reproducibility.
• Review automated graders for 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.
• Support consistent quality, productivity, and delivery across the trainer pod.

This is a remote contractor assignment expected to last approximately four weeks. Compensation follows the project's standard terms.

• Time: At least 40 hours per week, including at least four hours of daily commitment.
• Schedule: At least four hours of overlap with Pacific Time.
• Location: Remote and open worldwide.
• Language: Professional fluency in English.
• Education and experience: A 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, C++, Fortran, Julia, MATLAB, or Octave, plus Linux proficiency.
• Technical expertise: Hands-on experience 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, and review of complex technical work.
• Leadership: Experience mentoring or leading small technical teams.
• Helpful tools: OpenFOAM, SU2, CalculiX, FEniCS, Gmsh, FreeCAD, MuJoCo, ROS, OpenVSP, OpenMDAO, ANSYS, Abaqus, or NASTRAN.
• Helpful development experience: Docker, Git, CI/CD, automated testing, or AI coding agents.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training work is the human side of building artificial intelligence: people create, review, and test examples that help AI systems produce better results. Experienced engineers are needed for specialist projects because they can judge technical accuracy, sound reasoning, and reliable computational results.