Remote | Computational Mechanics & FEA Expert — $60–$75/hour

24-Mag•New York, NY

3d•Remote

About The Position

We are sharing a specialised part-time consulting opportunity for computational structural and mechanical engineering professionals with graduate-level expertise in numerical simulation, finite-element analysis, computational mechanics, and scientific software. This role supports the development of advanced AI benchmarks for research-level computational engineering. Selected experts will design original technical problems that require AI systems to use real scientific software, execute simulations, interpret numerical results, plan computational experiments, and reason through complex engineering workflows.

Requirements

Master's degree, PhD, or equivalent research experience in Mechanical Engineering, Structural Engineering, Computational Engineering, Applied Mechanics , or a closely related STEM discipline
Strong hands-on experience with computational structural or mechanical engineering software
Proven proficiency with at least one relevant open-source scientific computing or simulation framework
Professional or research experience using numerical methods to solve real engineering problems
Strong understanding of finite-element analysis, computational mechanics, PDE-based modelling, or related numerical disciplines
Strong Python programming skills
Ability to develop computational setups, reference solutions, and automated validators
Comfortable working in Linux and terminal-based environments
Ability to diagnose numerical edge cases, solver failures, and modelling limitations

Nice To Haves

Research publications, open-source contributions, or substantial professional simulation work are highly valued
Experience across multiple computational engineering tools or disciplines is advantageous
Familiarity with benchmark design, scientific teaching, or advanced problem-set development is beneficial
Experience with computational reproducibility or containerised environments is advantageous

Responsibilities

Create original graduate-level problems in structural, mechanical, thermal, and computational engineering
Develop tasks based on realistic scientific and engineering workflows
Design problems that require multi-step numerical reasoning rather than simple formula application
Construct tasks with clearly defined setups, expected outputs, and objective validation criteria
Refine problem difficulty based on model performance and evaluation results
Develop computational problems involving beam, plate, and shell analysis
Work with linear and nonlinear elasticity, continuum mechanics, and solid mechanics
Design tasks using finite-element and variational formulations
Incorporate mesh refinement, convergence studies, and numerical verification
Apply theories such as Euler–Bernoulli and Timoshenko beam formulations where relevant
Build problems requiring specialised open-source scientific software
Apply finite-element, finite-volume, Galerkin, PDE discretisation, and related numerical methods
Develop tasks involving constitutive modelling, numerical linear algebra, and nonlinear solution techniques
Evaluate numerical stability, convergence, accuracy, and modelling assumptions
Create workflows that reflect genuine computational research practice
Design tasks involving computational fluid dynamics and fluid mechanics
Develop thermal-fluid, heat-transfer, and mass-transfer simulations
Create problems involving thermodynamics, combustion, and HVAC or thermal systems
Work with coupled multiphysics simulations and engineering system models
Develop optimisation, reliability, and manufacturing-simulation problems where relevant
Apply hands-on expertise with 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
Develop problem setups and reference solutions using domain-specific computational tools
Work with other open-source structural, mechanical, and scientific solver frameworks where appropriate
Diagnose solver limitations, numerical edge cases, and implementation failure modes
Use Python and, where relevant, C, C++, or Fortran-based scientific codes
Test computational problems against advanced AI systems
Analyse whether models can correctly execute scientific workflows and interpret results
Design tasks where models must strategically choose simulations, measurements, or queries
Create challenges where hidden information must be inferred from partial computational results
Write reference implementations, oracle functions, and solution validators
Verify that benchmark answers are numerically and scientifically correct
Ensure computational tasks are reproducible across controlled environments
Document assumptions, parameters, boundary conditions, and expected outputs clearly
Work within Linux-based and remote computational environments