Remote Pod Lead - Mechanical & Aerospace Engineering

Turing

2d•Remote

About The Position

Turing is seeking an experienced Mechanical or Aerospace Engineering expert to lead a pod of approximately 5–10 technical trainers developing realistic, terminal-based engineering tasks for a frontier AI training and evaluation initiative. The role focuses on reviewing tasks for technical accuracy, reproducibility, and evaluation quality, ensuring that they reflect authentic mechanical and aerospace engineering workflows. The Pod Lead will mentor trainers, maintain quality standards, and drive consistent task delivery. The position involves end-to-end review of engineering tasks and their supporting files, simulations, reference solutions, and automated tests. The Pod Lead validates engineering calculations and computational environments, assesses whether graders measure meaningful engineering outcomes, identifies technical errors and unrealistic assumptions, and provides actionable feedback. They also onboard trainers, allocate work, monitor quality and throughput, escalate blockers, and maintain technical documentation and best practices. This is a contractor assignment for a four-week contract. The commitment is at least 40 hours per week, with at least four hours per day and four hours of overlap with PST. The expected start date is next week.

Requirements

Ph.D., postdoctoral experience, or equivalent advanced technical experience in Mechanical Engineering, Aerospace Engineering, or a closely related discipline.
Strong programming skills in Python, C/C++, Fortran, Julia, or MATLAB/Octave, with proficiency in Linux environments.
Hands-on expertise in at least one major area: FEA, CFD, dynamics, flight simulation, thermal modeling, astrodynamics, guidance, navigation and control (GNC), or propulsion modeling.
Strong understanding of engineering simulation, numerical methods, computational modeling, and technical validation.
Experience reviewing complex technical work, engineering simulations, computational models, or research outputs.
Ability to evaluate engineering assumptions, numerical accuracy, solver convergence, and the reliability of computational results.
Strong analytical judgment and ability to identify technical errors, 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.
Commitment of at least 40 hours per week, at least 4 hours per day, and 4 hours of overlap with PST.

Nice To Haves

Experience with engineering simulation and computational tools such as OpenFOAM, SU2, CalculiX, Code_Aster, FEniCS, Elmer, Gmsh, FreeCAD, CadQuery, MuJoCo, ROS, XFOIL, AVL, OpenVSP, JSBSim, OpenMDAO, pyCycle, poliastro, Orekit, GMAT, or SPICE.
Familiarity with commercial engineering software such as ANSYS, Abaqus, COMSOL, or NASTRAN, including reproducing workflows using open-source tools.
Experience with state estimation, trajectory optimization, topology optimization, aeroelastic analysis, or multidisciplinary design optimization.
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.
Industry or agency experience in automotive, energy, manufacturing, robotics, aircraft, launch vehicles, satellites, or UAVs.
Prior experience in AI training, technical quality control, benchmark development, or evaluation programs.

Responsibilities

Lead and mentor a pod of technical trainers, ensuring consistent quality, productivity, and timely delivery of engineering tasks.
Review engineering tasks end to end, including problem statements, geometry and mesh files, simulation inputs, material properties, reference solutions, and automated tests.
Validate tasks involving FEA, CFD, thermodynamics, heat transfer, dynamics, robotics, flight simulation, propulsion, and orbital mechanics.
Ensure tasks reflect realistic mechanical and aerospace engineering workflows and require meaningful multi-step technical reasoning.
Verify engineering correctness across units, equilibrium, conservation laws, boundary conditions, mesh convergence, solver tolerances, reference frames, and numerical stability.
Validate computational environments, simulation tools, solvers, and dependencies to ensure reproducible results.
Review automated graders to ensure they evaluate meaningful engineering outputs, such as stresses, deflections, natural frequencies, aerodynamic coefficients, thermal performance, orbital parameters, and safety margins.
Identify technical inaccuracies, unrealistic assumptions, incorrect tolerances, and opportunities to bypass engineering calculations or simulations.
Provide clear, actionable technical feedback, track revisions, and ensure quality issues are resolved before task approval.
Onboard trainers, allocate work, monitor quality and throughput, escalate blockers, and maintain technical documentation and best practices.