Remote Structural & Mechanical Engineering Expert - AI Trainer

Structural & Mechanical Engineering Expert

About the Project:

We are building a large‑scale benchmark to test how well advanced AI systems can solve hard scientific and engineering problems. As a task designer, you will create challenging computational problems that require the use of real scientific software to perform research‑level work—running simulations, interpreting results, designing experiments, and uncovering hidden information from data. This is not a typical data‑labeling job; instead you will 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 appropriate.

You will create problems that require skilled use of specialized scientific software. Some problems 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 more challenging: the AI must plan a series of queries or experiments to uncover information that is not directly visible, which requires strategic thinking about what to measure, how to read partial results, and how to narrow down possibilities efficiently.

Each problem will undergo a testing loop against state‑of‑the‑art AI models, and you will refine it until it hits the target difficulty.

We’re especially interested in experts with deep, hands‑on experience in Structural & Mechanical Engineering—working with scikit‑fem or similar finite element libraries for beam analysis, elasticity problems, and computational mechanics. Experience with Timoshenko beam theory, mesh convergence studies, or variational formulations is valuable. Experience with other specialized software in this domain will also be considered.

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 have written code with these libraries to solve actual research problems, understand where they break, their edge cases, and what makes a problem genuinely hard rather than merely complicated. Beyond domain expertise, strong candidates think like puzzle designers: they build problems where the challenge comes from smart reasoning rather than raw computation, where several approaches seem plausible but only careful analysis reveals the correct one, and where surface‑level pattern matching will not yield the answer.

• 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, demonstrated through research publications, open‑source contributions, or professional work
• Strong Python skills—you will 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

• 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 containerised environments

Please note:

This application includes a coding assessment as part of the evaluation process.