Mechanical & Aerospace Engineering Pod Lead, Terminal Bench

The work

You will lead review work for AI training focused on mechanical and aerospace engineering simulation. You will assess engineering tasks, simulation files, reference solutions, automated tests, and model outputs using project guidelines.

Your feedback will help improve model behavior and maintain consistent quality across repeated technical review tasks.

• Evaluate model outputs and apply project rubrics.
• Review source materials and technical results against project guidelines.
• Document evidence-based judgments and explain them clearly.
• Identify technical errors, edge cases, flawed assumptions, numerical issues, and solver convergence problems.
• Maintain consistent quality across review tasks.
• Mentor, review, or lead small technical teams.

What it pays and takes

No compensation rate is provided in the role details.

• Schedule: At least 4 hours per day and 40 hours per week.
• Time-zone requirement: Four hours of overlap with Pacific Standard Time.
• Work arrangement: Part-time contractor role.
• Language: Professional fluency in English.
• Education or 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, or MATLAB/Octave, with proficiency in Linux environments.
• Technical focus: Hands-on experience in at least one of FEA, CFD, dynamics, flight simulation, thermal modeling, astrodynamics, guidance, navigation and control, or propulsion modeling.
• Knowledge: Engineering simulation, numerical methods, computational modeling, and technical validation.
• Review experience: Experience assessing complex technical work, engineering simulations, computational models, or research outputs.
• Communication: Strong written communication for precise technical feedback and quality documentation.

Helpful technical background

Experience with engineering simulation and computational tools is useful. Relevant tools include OpenFOAM, SU2, CalculiX, Code_Aster, FEniCS, Elmer, Gmsh, FreeCAD, CadQuery, MuJoCo, ROS, XFOIL, AVL, OpenVSP, JSBSim, OpenMDAO, pyCycle, poliastro, Orekit, GMAT, and SPICE.

Experience with commercial tools such as ANSYS, Abaqus, COMSOL, or NASTRAN is also helpful, especially when reproducing workflows with open-source software.

• State estimation
• Trajectory optimization
• Topology optimization
• Aeroelastic analysis
• Multidisciplinary design optimization

How it works

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

About AI training work

AI training work is the human process of preparing examples, reviewing model outputs, and giving clear feedback so artificial intelligence systems improve. People with specialized engineering knowledge are needed because they can judge technical accuracy, assumptions, and whether computational results are reliable.

Where you can work

This role is 100% remote: you can work from any country except Andorra, Afghanistan, Barbados, Burkina Faso, Burundi, Brazil, Belarus, Belize, DR Congo, Central African Republic, Congo (Republic), Cameroon, China, Cuba, Cape Verde, Djibouti, Dominica, Eritrea, Fiji, Micronesia, Grenada, Georgia, Guinea, Equatorial Guinea, Guinea-Bissau, Honduras, Haiti, Iraq, Iran, Kyrgyzstan, Kiribati, Comoros, St. Kitts and Nevis, North Korea, Lebanon, Liberia, Lesotho, Libya, Montenegro, Marshall Islands, Mali, Myanmar, Mauritania, Maldives, Malawi, Nicaragua, Nepal, Nauru, Papua New Guinea, Palestine, Palau, Russia, Solomon Islands, Seychelles, Sudan, Sierra Leone, Somalia, Suriname, South Sudan, São Tomé and Príncipe, Syria, Eswatini, Chad, Togo, Tajikistan, Timor-Leste, Turkmenistan, Tonga, Tuvalu, Ukraine, Uganda, Vatican City, St. Vincent and Grenadines, Venezuela, Vanuatu, Samoa, Yemen, Zambia and Zimbabwe.