The Work
You will lead quality review for technical tasks used to evaluate AI systems. The work covers civil and structural engineering, including structural and geotechnical models, load cases, finite-element analysis, earthquake and wind engineering, hydraulics, hydrology, transportation, and infrastructure systems.
You will review engineering work from end to end, check whether the analysis is accurate and reproducible, and give precise feedback to the people building the tasks. You will also mentor trainers, assign work, monitor quality, track revisions, and resolve technical blockers.
- Review problem statements, 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, dependencies, simulation results, and automated graders against forces, deflections, drifts, settlements, flow rates, factors of safety, and structural performance.
- Find technical errors, incorrect tolerances, edge cases, and ways a task could bypass meaningful engineering analysis.
- Review work involving nonlinear time-history analysis, performance-based design, reliability analysis, structural health monitoring, and related methods when applicable.
What It Pays And Takes
This is a remote contractor assignment lasting four weeks. The role is open worldwide and requires professional fluency in English.
- Pay: The listing does not provide a rate.
- Time requirement: 20+ hours per week.
- Schedule: At least 4 hours per day and 4 hours of overlap with Pacific Time.
- Education and experience: 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, plus Linux proficiency.
- Engineering expertise: 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.
- Helpful experience: Reviewing complex technical work, engineering simulations, research outputs, or computational models, and mentoring or leading small technical teams.
Helpful Tools And Background
Experience with engineering and simulation tools is useful, but the listing does not require a specific tool. Familiarity with software used for modeling, testing, and technical workflows can help you review tasks more effectively.
- OpenSees, Code_Aster, CalculiX, FEniCS, Gmsh, PyNite, EPANET, SWMM, HEC-RAS, MODFLOW, SUMO, QGIS, GDAL, SAP2000, ETABS, Abaqus, PLAXIS, or STAAD.Pro.
- Docker, Git, CI/CD pipelines, and automated testing.
- AI coding agents and computational engineering workflows.
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-reviewed examples to help artificial intelligence systems learn and perform better. Experienced engineers are needed to check technical reasoning, simulations, and evaluation tasks that require real subject knowledge.