Mechanical & Aerospace AI Training Pod Lead

Crossing Hurdles; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Crossing Hurdles seeks a Mechanical & Aerospace Engineering Pod Lead to guide a team of technical trainers focused on creating terminal-based tasks for AI training and evaluation. You will ensure technical accuracy, reproducibility and reliable automated grading across problems and workflows.

You will review problem statements, meshes, solver inputs and reference solutions, mentor trainers, and oversee QA to prevent bypassed computations while enabling efficient delivery of high-quality content.

Qualifications

• Ph.D., postdoctoral experience or equivalent advanced technical experience in Mechanical Engineering, Aerospace Engineering or a related field.
• Strong Python, C/C++, Fortran, Julia or MATLAB/Octave skills, with Linux proficiency.
• Expertise in FEA, CFD, dynamics, flight simulation, thermal modeling, astrodynamics, GNC or propulsion modeling.
• Experience reviewing simulations, computational models or research outputs and leading small technical teams.
• Strong numerical methods, technical judgment and written communication.

Responsibilities

• Review problem statements, geometry and meshes, simulation inputs, material properties, reference solutions and tests.
• Validate FEA, CFD, heat transfer, dynamics, flight simulation, propulsion and orbital mechanics workflows.
• Verify units, conservation laws, boundary conditions, mesh convergence, solver tolerances and numerical stability.
• Review graders for meaningful engineering outputs and prevent bypassed computations.
• Mentor trainers, provide actionable feedback, allocate work and track quality.

Skills

Python

C/C++

Fortran

Julia

MATLAB/Octave

Linux

Education

Ph.D. or equivalent advanced technical experience

Tools

OpenFOAM

SU2

CalculiX

FEniCS

Gmsh

MuJoCo

ROS

OpenMDAO

ANSYS

Abaqus

COMSOL

Docker

Git

Job description

Crossing Hurdles seeks a Mechanical & Aerospace Engineering Pod Lead to guide a team of technical trainers focused on creating terminal-based tasks for AI training and evaluation. You will ensure technical accuracy, reproducibility and reliable automated grading across problems and workflows.

You will review problem statements, meshes, solver inputs and reference solutions, mentor trainers, and oversee QA to prevent bypassed computations while enabling efficient delivery of high-quality content.