Computational Mechanics Expert - PhD - AI Trainer

Computational Structural & Mechanical Engineering Expert About the Project We're building a large-scale benchmark to test how well advanced AI systems can solve hard computational scientific and engineering problems. As a task designer, you'll create challenging computational problems that check whether AI can use real scientific software to do research-level work — running simulations, interpreting results, designing experiments, and uncovering hidden information from data. This isn't a typical data-labeling job. You'll design original, graduate-level problems based on real scientific workflows, test them against cutting-edge AI models, and fine-tune them until the difficulty is just right. What You'll Do You'll create problems that require skilled use of specialized scientific software. Some will ask the AI to compute exact answers from a fully defined setup — testing whether it can correctly carry out complex, multi-step workflows. Others will be harder: the AI must plan a series of queries or experiments to uncover information that isn't directly visible, which means thinking strategically about what to measure, how to read partial results, and how to narrow down the possibilities efficiently. Each problem goes through a testing loop against state-of-the-art AI models, and you'll refine it until it hits the target difficulty. Domains & Tools We're Hiring For We're especially interested in experts with deep, hands-on experience with open-source, domain-specific computational 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 , for finite-element analysis, computational mechanics, structural analysis, elasticity, CFD, multiphysics simulation, heat and mass transfer, thermodynamics, combustion, fluid mechanics, HVAC/thermal systems, manufacturing simulation, optimization, or thermophysical-property calculations. Relevant work may include beam, plate, and shell analysis; linear or nonlinear elasticity; finite-element and variational formulations; mesh refinement and convergence studies; continuum and solid mechanics; computational fluid dynamics; coupled multiphysics problems; thermal-fluid simulation; structural or system optimization; reliability analysis; and related numerical engineering workflows . Experience with underlying theories and numerical methods — such as Euler–Bernoulli and Timoshenko beam theory, continuum mechanics, finite-element methods, Galerkin/variational methods, finite-volume methods, PDE discretization, constitutive modeling, thermodynamics, numerical linear algebra, and nonlinear solution methods — is valuable. Experience with other open-source computational structural or mechanical engineering software will also be considered, including scientific codes and solver frameworks built with Python, C, C++, or Fortran . What Makes a Strong Candidate You have graduate-level expertise (MS or PhD preferred) in the domain above, with real hands-on experience using these tools — not just theoretical knowledge. You've written code using these libraries to solve actual research problems, and you understand where they break, what their edge cases are, and what makes a problem genuinely hard rather than just complicated. Beyond domain expertise, the best candidates think like puzzle designers: building problems where the challenge comes from smart reasoning rather than raw computation, where several approaches seem plausible but only careful analysis reveals the right one, and where surface-level pattern matching won't get you to the answer. Requirements Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience) Proven proficiency with at least one of the listed scientific software libraries, shown through research publications, open-source contributions, or professional work Strong Python skills — you'll be writing problem setups, oracle functions, and solution validators Ability to work independently and refine problem designs based on feedback Comfortable working in a Linux/terminal environment with remote compute sandboxes Available for at least 15–20 hours per week Nice to Have Experience across multiple listed domains or tools Familiarity with benchmark or evaluation design Background in scientific teaching or exam/problem-set design Experience with computational reproducibility and containerized environments #J-18808-Ljbffr Mercor