Role Overview: We are seeking experienced Civil or Structural Engineering experts with strong computational and programming skills to lead a team of 510 technical trainers developing challenging engineering tasks for advanced AI training and evaluation. The role involves reviewing engineering models, simulations, computational workflows, and automated graders for technical accuracy and reproducibility, while mentoring trainers and maintaining high-quality standards.
Responsibilities:
- Lead a pod of approximately 510 trainers.
- Review engineering tasks and simulation outputs.
- Validate reference solutions and automated graders.
- Ensure technical accuracy and reproducibility.
- Maintain quality standards throughout task development.
Requirements:
- Ph.D., postdoctoral experience, or equivalent advanced technical expertise in Civil Engineering, Structural Engineering, Geotechnical Engineering, or a closely related discipline.
- Strong programming skills in Python, C/C++, Julia, Fortran, or MATLAB/Octave.
- Proficiency in Linux and terminal-based environments.
- Hands-on expertise in at least one area such as structural analysis, finite-element analysis (FEA), earthquake engineering, geotechnical modeling, or hydraulic/hydrological modeling.
- Strong understanding of engineering principles, numerical methods, computational simulations, and model validation.
- Working knowledge of engineering design standards such as ASCE 7, ACI, AISC, Eurocodes, or IS codes.
- Experience reviewing engineering simulations, computational models, research outputs, or complex technical work.
- Experience mentoring, reviewing, or leading small technical or research teams.
- Excellent written communication skills and the ability to provide precise, constructive technical feedback.
Preferred Qualifications:
- Familiarity with engineering tools such as OpenSees/OpenSeesPy, Code_Aster, CalculiX, FEniCS, Gmsh, EPANET, SWMM, HEC-RAS, or MODFLOW.
- Experience with commercial engineering software such as SAP2000, ETABS, Abaqus, PLAXIS, or STAAD.Pro.
- Experience with nonlinear time-history analysis, performance-based design, reliability analysis, or structural health monitoring.
- Familiarity with Docker, Git, CI/CD pipelines, and automated testing.
- Experience developing or evaluating AI coding agents, terminal-based agents, or AI-generated engineering solutions.
- Professional engineering licensure such as PE, SE, CEng, or equivalent.
- Industry experience in buildings, bridges, transportation networks, water systems, or major infrastructure projects.
- Prior experience in AI training, technical quality control, benchmark development, or evaluation programs.
Project Details:
- Commitment: Full-time, minimum 40 hours/week, with at least 4 hours overlap with Pacific Standard Time.
- Duration: 4 weeks.
- Employment Type: Contractor assignment (no medical or paid leave).
