– Pod Lead: Mechanical & Aerospace Engineering

Contract Duration: 4 weeks (initially) - (Extendable)

Role Overview

We are seeking an experienced Mechanical or Aerospace Engineering expert to lead a team of 5–10 technical trainers developing realistic, terminal-based engineering tasks for an AI training and evaluation initiative. The role involves reviewing tasks for technical accuracy, reproducibility, and evaluation quality while mentoring trainers and ensuring consistent delivery standards.

Key Responsibilities

- Lead and mentor a pod of 5–10 technical trainers, ensuring quality, productivity, and timely delivery.
- Review engineering tasks, 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 engineering workflows and require meaningful multi-step technical reasoning.
- Verify units, equilibrium, conservation laws, boundary conditions, mesh convergence, solver tolerances, reference frames, and numerical stability.
- Validate computational environments, simulation tools, solvers, and dependencies for reproducibility.
- Review automated graders for meaningful engineering outputs, including 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 actionable technical feedback and ensure quality issues are resolved before task approval.
- Onboard trainers, allocate tasks, monitor quality and throughput, resolve blockers, and maintain technical documentation and best practices.

Required Qualifications

- 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 computational reliability.
- Solid analytical judgment 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.

Preferred Qualifications

- Experience with 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 with 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.

📌 Mechanical & Aerospace Engineer (India)
🏢 Sourcebae
📍 India