Remote Pod Lead - Mechanical & Aerospace Engineering

Turing is a rapidly growing AI company focused on advancing and deploying cutting-edge AI systems. The company collaborates with leading AI labs to push the boundaries of model capabilities in areas such as reasoning, coding, multimodality, and STEM knowledge. These advancements are then applied to develop real-world AI solutions that address critical business challenges.

Key Responsibilities:
Lead and mentor a team of 5 to 10 technical trainers who develop realistic, terminal-based engineering tasks for an AI training and evaluation initiative. Oversee the creation and review of engineering tasks to ensure technical accuracy, reproducibility, and high evaluation standards. 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 authentic mechanical and aerospace engineering workflows and require multi-step technical reasoning. Verify engineering correctness across units, equilibrium, conservation laws, boundary conditions, mesh convergence, solver tolerances, reference frames, and numerical stability. Confirm the reliability of computational environments, simulation tools, solvers, and dependencies to guarantee reproducible results. Review automated graders to ensure meaningful evaluation of 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 potential shortcuts in engineering calculations or simulations. Provide clear, actionable technical feedback, track revisions, and ensure quality issues are resolved prior to task approval. Manage trainer onboarding, work allocation, quality monitoring, throughput, escalation of blockers, and maintenance of technical documentation and best practices.

Required Qualifications:
Ph.D., postdoctoral experience, or equivalent advanced expertise in Mechanical Engineering, Aerospace Engineering, or a closely related field. Strong programming skills in Python, C/C++, Fortran, Julia, or MATLAB/Octave, with proficiency in Linux environments. Hands-on experience in at least one core area such as FEA, CFD, dynamics, flight simulation, thermal modeling, astrodynamics, guidance, navigation and control (GNC), or propulsion modeling. Deep 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 critically evaluate engineering assumptions, numerical accuracy, solver convergence, and computational result reliability. Strong analytical judgment to detect technical errors, edge cases, and flawed engineering assumptions. Experience mentoring, reviewing, or leading small technical teams. Excellent written communication skills for delivering precise technical feedback and documenting quality standards.

Preferred Qualifications and Benefits:
Experience with open-source 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 like ANSYS, Abaqus, COMSOL, or NASTRAN, including the ability to replicate workflows using open-source alternatives. Knowledge of state estimation, trajectory optimization, topology optimization, aeroelastic analysis, or multidisciplinary design optimization. Experience with Docker, Git, CI/CD pipelines, and automated testing. Background in developing or evaluating AI coding agents, terminal-based agents, or large language model (LLM)-generated engineering solutions. Industry experience in automotive, energy, manufacturing, robotics, aircraft, launch vehicles, satellites, or UAVs is a plus. Prior involvement in AI training, technical quality control, benchmark development, or evaluation programs is advantageous.

Offer Details:
This is a contractor assignment requiring a minimum commitment of 40 hours per week, with at least 4 hours daily and a 4-hour overlap with Pacific Standard Time (PST). The contract duration is 4 weeks, with an expected start date in the upcoming week. Please note that this role does not include medical benefits or paid leave.