Structural & Mechanical Engineering Computational Problem Designer [Remote]

This role focuses on creating a large-scale evaluation benchmark for advanced AI reasoning in scientific and engineering domains. As a task designer, you will develop challenging computational problems that assess AI systems'' ability to utilize real scientific software tools for solving research-grade problems, including querying simulations, interpreting outputs, and designing experimental strategies.

This position is distinct from typical annotation or labeling roles. You will design original, graduate-level computational problems based on real scientific workflows, calibrate them against cutting-edge AI models, and iterate on the designs to ensure appropriate difficulty levels.

• Design problems that require advanced use of domain-specific scientific software libraries.
• Create tasks that involve computing precise outputs from fully specified setups, testing solvers on complex multi-step scientific workflows.
• Develop sequences of queries or experiments to uncover hidden information, requiring strategic reasoning about measurements and interpretations.
• Engage in a calibration loop to test each task against state-of-the-art AI models and refine problem designs based on feedback.

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

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

The ideal candidate will possess graduate-level expertise (MS or PhD preferred) in the relevant domain, with practical experience using specific software tools. You should have:

• Written code that utilizes these libraries to solve real research problems, understanding their limitations and edge cases.
• The ability to think like a puzzle designer, creating problems where difficulty arises from reasoning strategy rather than simple computation.
• A knack for constructing problems that require careful analysis to identify the correct approach among multiple plausible solutions.

• Graduate-level training in a relevant STEM domain (MS, PhD, or equivalent research experience).
• Demonstrated proficiency with at least one of the specified scientific software libraries, supported 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 iterate on problem designs based on calibration feedback.
• Comfort with working in a Linux/terminal environment and 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 pedagogy or exam/problem-set design.
• Experience with computational reproducibility and containerized environments.