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

Requisitos

• 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

Desejáveis

• 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, OpenMDA.
• 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