Pod lead - Mechanical & Aerospace Engineering (Terminal Bench Science)

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 Mechanical or Aerospace Engineering expert to lead a team of technical trainers developing realistic, terminal-based engineering tasks for a frontier AI training and evaluation initiative.

In this role, you will oversee a pod of approximately 5–10 trainers, review engineering tasks for technical accuracy, reproducibility, and evaluation quality, and ensure that tasks reflect authentic mechanical and aerospace engineering workflows. You will also mentor trainers, maintain quality standards, and drive consistent task delivery.

What you'll do:

• 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 finite-element analysis (FEA), computational fluid dynamics (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.

What we're looking for:

• 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 with the 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.

Nice to have:

• 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.

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]