Structural & Mechanical Engineer for AI Problem Design

This role offers a unique opportunity to contribute to a large-scale benchmark project aimed at assessing the capabilities of advanced AI systems in solving complex scientific and engineering problems. As a task designer, you will create original, graduate-level computational challenges that evaluate AI''s ability to utilize real scientific software for research-level tasks, including running simulations, interpreting results, designing experiments, and extracting hidden insights from data.

• Design problems that require proficient use of specialized scientific software, ensuring they test AI''s ability to perform complex, multi-step workflows.
• Create challenges where AI must strategically plan queries or experiments to uncover non-visible information, necessitating careful measurement and analysis.
• Engage in a testing loop with state-of-the-art AI models, refining problems to achieve the appropriate level of difficulty.

We are particularly interested in candidates with extensive, hands-on experience in:

• Structural & Mechanical Engineering, familiarity with scikit-fem or similar finite element libraries for beam analysis, elasticity problems, and computational mechanics. Knowledge of Timoshenko beam theory, mesh convergence studies, or variational formulations is advantageous.
• Experience with other specialized software in this domain will also be considered.

The ideal candidate possesses graduate-level expertise (MS or PhD preferred) in the relevant domain, with practical experience using the specified tools. You have a proven track record of writing code with these libraries to solve real research problems, understanding their limitations and the nuances that make a problem genuinely challenging.

Moreover, you approach problem design like a puzzle creator, focusing on challenges that require intelligent reasoning rather than mere computation. You craft problems where multiple plausible solutions exist, but only thorough analysis leads to the correct answer.

• Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
• Demonstrated proficiency with at least one of the specified scientific software libraries, evidenced by research publications, open-source contributions, or professional experience.
• Strong Python programming skills for writing problem setups, oracle functions, and solution validators.
• Ability to work independently and iteratively refine problem designs based on feedback.
• Comfortable operating in a Linux/terminal environment with remote compute sandboxes.
• Availability 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 containerized environments.

Please note that this application includes a coding assessment as part of the evaluation process.