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]