The Work
You will guide trainers who create realistic, terminal-based technical tasks for AI evaluation. Your work covers civil and structural engineering problems, including structural and geotechnical models, finite-element analysis, earthquake and wind engineering, hydraulics, hydrology, transportation, and infrastructure systems.
You will review each task from the problem statement through the reference solution and automated tests. You will also give precise feedback, track revisions, mentor trainers, allocate work, monitor quality, and resolve technical blockers.
- Review engineering models, load cases, simulation data, computational environments, reference solutions, and automated tests.
- Check units, equilibrium, boundary conditions, load combinations, design codes, numerical stability, convergence, safety factors, and engineering assumptions.
- Validate solvers, software dependencies, simulation results, and automated graders.
- Assess outputs such as forces, deflections, drifts, settlements, factors of safety, flow rates, and structural performance.
- Identify technical errors, incorrect tolerances, edge cases, and ways to bypass meaningful engineering analysis.
What It Pays And Takes
The role details do not specify a pay rate. This is a remote contractor assignment lasting four weeks, with flexible part-time classification and a commitment of 20 or more hours per week.
- Schedule: At least 20 hours per week, including at least 4 hours per day and 4 hours of overlap with Pacific Time.
- Location: Remote and open worldwide.
- Education: A Ph.D., postdoctoral experience, or equivalent advanced technical experience in civil engineering, structural engineering, geotechnical engineering, or a closely related field.
- Programming: Strong experience with Python, C/C++, Julia, Fortran, or MATLAB/Octave.
- Technical skills: Linux, structural analysis, finite-element methods, earthquake engineering, geotechnical modeling, or hydraulic and hydrological modeling.
- Knowledge: Engineering principles, numerical methods, simulation workflows, model validation, and standards such as ASCE 7, ACI, AISC, Eurocodes, or IS codes.
- Experience reviewing technical work, engineering simulations, research outputs, or computational models is valuable.
- Experience mentoring or leading small technical teams is valuable.
Helpful Technical Background
Experience with engineering software and technical workflows can help you succeed in this role. Familiarity with the tools and methods below is useful but not listed as required.
- Engineering tools: OpenSees, Code_Aster, CalculiX, FEniCS, Gmsh, PyNite, EPANET, SWMM, HEC-RAS, MODFLOW, SUMO, QGIS, GDAL, SAP2000, ETABS, Abaqus, PLAXIS, or STAAD.Pro.
- Development workflows: Docker, Git, CI/CD pipelines, and automated testing.
- Specialized methods: Nonlinear time-history analysis, performance-based design, reliability analysis, and structural health monitoring.
- Other experience: 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 review and examples to help artificial intelligence systems learn. In this role, your engineering judgment helps assess whether technical tasks, simulations, and automated evaluations reflect accurate and reproducible reasoning.