Computational Structural & Mechanical Engineering Expert

$70–85/hr

50 people hired

RemoteGlobalResearch3-5 YearsContractPosted Aug 13, 2026

About this role

You will design advanced, graduate-level computational engineering problems to evaluate how well AI systems can perform real scientific and engineering workflows. The work involves creating original research-style tasks requiring simulation, numerical modeling, experiment design, result interpretation, optimization, and data-driven reasoning using open-source computational tools. You will test problems against state-of-the-art AI models and refine them until they achieve the required level of technical difficulty, reliability, and reasoning complexity.

Responsibilities

Computational problem design: Create original graduate- and research-level structural, mechanical, thermal, fluid, and multiphysics engineering problems.
Scientific simulation: Develop tasks requiring AI systems to execute simulations, analyze outputs, design experiments, and infer hidden information from partial results.
Tool-based workflows: Build problems using open-source scientific tools such as FEniCSx/DOLFINx, scikit-fem, OpenFOAM, deal.II, MFEM, MOOSE, CalculiX, Elmer FEM, Code_Aster, SfePy, FiPy, Devito, Cantera, CoolProp, Pyomo, or SimPy.
Python development: Write simulation setups, oracle functions, validation scripts, and solution-checking logic.
AI benchmark testing: Test tasks against advanced AI models and refine problem difficulty based on model performance.
Numerical validation: Verify numerical accuracy, solver behavior, convergence, assumptions, edge cases, and reproducibility.
Research workflow development: Design tasks involving finite-element analysis, CFD, elasticity, structural analysis, thermodynamics, heat transfer, optimization, reliability, and multiphysics simulation.

Qualifications

Master’s, PhD, or equivalent research experience in Mechanical Engineering, Structural Engineering, Computational Engineering, Aerospace Engineering, Applied Mechanics, Computational Science, or a closely related STEM field.
Proven hands-on proficiency with at least one relevant open-source scientific or engineering software library, demonstrated through research publications, open-source contributions, or professional work.
Strong knowledge of computational mechanics, finite-element methods, structural analysis, CFD, multiphysics simulation, heat and mass transfer, thermodynamics, optimization, or related numerical engineering areas.
Strong understanding of numerical methods such as finite-element methods, finite-volume methods, Galerkin/variational methods, PDE discretization, constitutive modeling, numerical linear algebra, and nonlinear solution techniques.
Strong Python programming skills for creating computational setups, oracle functions, validators, and automated solution checks.
Comfortable working with Linux, terminal environments, and remote computational sandboxes.
Ability to independently design, test, debug, and refine technically challenging computational problems.
Experience with tools or solver frameworks developed in Python, C, C++, or Fortran is valuable.