About this role · Engineering
About Turing:
Turing is one of the world’s fastest-growing AI companies, accelerating the advancement and deployment of powerful AI systems.
Turing helps customers in two ways: Working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilinguality, STEM and frontier knowledge; and leveraging that work to build real-world AI systems that solve mission-critical priorities for companies.
Role Overview:
We are seeking an experienced Civil or Structural Engineering expert to lead a pod of trainers building realistic, terminal-based technical tasks for Terminal Bench Science. You will review every task your trainers produce for engineering correctness, reproducibility, and grader reliability, and coach trainers to consistently deliver high-quality tasks.
Reporting Structure:
Reports to the Civil & Structural Engineering Team Lead. Leads a pod of approximately 5–10 trainers.
What you'll do:
- Review engineering tasks end to end, including problem statements, structural and geotechnical models, load cases, simulation data, computational environments, reference solutions, and automated tests.
- Validate tasks involving structural analysis, finite-element methods (FEA), earthquake and wind engineering, structural dynamics, geotechnical modeling, hydraulics, hydrology, transportation, and infrastructure systems.
- Ensure tasks require genuine multi-step engineering reasoning and reflect realistic civil and structural engineering workflows.
- Verify engineering accuracy across units, equilibrium, boundary conditions, load combinations, design codes, numerical stability, convergence, and safety factors.
- Validate computational models, solvers, dependencies, and simulation results for reproducibility and technical correctness.
- Review automated graders to ensure they evaluate meaningful engineering outputs such as forces, deflections, drifts, settlements, factors of safety, flow rates, and structural performance.
- Identify technical errors, unrealistic assumptions, incorrect tolerances, and opportunities to bypass engineering analysis.
- Provide clear, actionable feedback to task developers and track revisions through completion.
- Mentor and support technical trainers, allocate work, monitor quality and productivity, and resolve technical blockers.
- Maintain quality standards, technical documentation, and best practices across engineering task development.
What we're looking for:
- Ph.D., postdoctoral experience, or equivalent advanced technical experience in Civil Engineering, Structural Engineering, Geotechnical Engineering, or a closely related discipline.
- Strong programming experience in Python, C/C++, Julia, Fortran, or MATLAB/Octave, with proficiency in Linux environments.
- Hands-on expertise in at least one major area: structural analysis, FEA, earthquake engineering, geotechnical modeling, or hydraulic/hydrological modeling.
- Strong understanding of engineering principles, numerical methods, simulation workflows, and model validation.
- Working knowledge of engineering design standards such as ASCE 7, ACI, AISC, Eurocodes, or IS codes.
- Experience reviewing complex technical work, engineering simulations, research outputs, or computational models.
- Strong analytical judgment with the ability to identify technical inaccuracies, edge cases, and flawed engineering assumptions.
- Experience mentoring, reviewing, or leading small technical teams.
- Excellent written communication skills for providing precise technical feedback and documenting quality standards.
Nice to have:
- Experience with engineering simulation and analysis tools such as OpenSees/OpenSeesPy, Code\_Aster, CalculiX, FEniCS, Gmsh, PyNite, EPANET, SWMM, HEC-RAS, MODFLOW, SUMO, QGIS, or GDAL.
- Familiarity 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 LLM-generated engineering solutions.
- Professional engineering licensure such as PE, SE, CEng, or equivalent.
- Industry experience working on buildings, bridges, transportation networks, water systems, or major infrastructure projects.
- Prior experience in AI training, technical quality control, benchmark development, or evaluation programs.
Offer Details:
- Commitments Required: At least 4 hours per day and minimum 40 hours per week with overlap of 4 hours with PST.
- Employment type : Contractor assignment (no medical/paid leave)
- Duration of contract : 4 week [expected start date is next week]
