Pod Lead - Civil and structural Engineering

Turing

About The Position

Turing is an AI company that works with leading AI labs to advance frontier model capabilities and builds real-world AI systems for companies. The Pod Lead will lead a pod of approximately 5–10 trainers developing realistic, terminal-based technical tasks for Terminal Bench Science. The role reports to the Civil & Structural Engineering Team Lead and is responsible for reviewing every task produced by the pod for engineering correctness, reproducibility, and grader reliability, while coaching trainers to deliver high-quality work. The role reviews tasks end to end, including problem statements, engineering models, load cases, simulation data, computational environments, reference solutions, and automated tests. It validates tasks across civil and structural engineering domains, ensures realistic multi-step engineering reasoning, verifies models and simulation results, and assesses automated graders for meaningful engineering outputs. The Pod Lead identifies errors and flawed assumptions, provides actionable feedback, tracks revisions, mentors trainers, allocates work, monitors quality and productivity, resolves technical blockers, and maintains documentation and quality standards. This is a contractor assignment lasting four weeks. The commitment is at least four hours per day and a minimum of 40 hours per week, with four hours of overlap with PST. The expected start date is next week. The assignment does not include medical or paid leave.

Requirements

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, and 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 and ability to identify technical inaccuracies, edge cases, and flawed engineering assumptions.
Experience mentoring, reviewing, or leading small technical teams.
Excellent written communication skills for precise technical feedback and documentation of quality standards.
Commitment of at least four hours per day and a minimum of 40 hours per week, with four hours of overlap with PST.

Nice To Haves

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.

Responsibilities

Lead a pod of approximately 5–10 trainers developing terminal-based technical tasks for Terminal Bench Science.
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, 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 ways 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 for engineering task development.