Remote Pod Lead - Civil and structural 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]