Remote STEM Computational Scientific Software & Evaluation Design - Electrical Engineering & RF

About the Project

We're building a large‑scale evaluation benchmark for advanced AI reasoning across scientific and engineering domains. The project involves designing computational problems that test whether AI systems can use real scientific software tools to solve research‑grade problems, including querying simulations, interpreting outputs, designing experimental strategies, and recovering hidden information from data. It is not a typical annotation or labeling role. The role requires designing original, graduate‑level computational problems grounded in real scientific workflows, calibrating them against frontier AI models, and iterating on problem design until the difficulty is optimal.

You will design problems that require sophisticated use of domain‑specific scientific software libraries. Some tasks will require computing precise outputs from fully specified setups, testing whether a solver can correctly implement complex multi‑step scientific workflows. Others will require designing a sequence of queries or experiments to uncover information that isn’t directly visible, demanding strategic reasoning about what to measure, how to interpret partial observations, and how to narrow down possibilities efficiently.

Each task goes through a calibration loop where it is tested against state‑of‑the‑art AI models, and you will refine the problem design to hit the target difficulty range.

We’re especially interested in experts with deep, hands‑on experience in the following area:

• Electrical Engineering & RF / Circuit Design – experience with scikit‑rf for RF and microwave network analysis, S‑parameter characterization, transmission‑line modeling, or ngspice for circuit simulation, operating point analysis, and frequency response characterization. Candidates should be comfortable designing problems that involve recovering circuit parameters from measurement data.
• Experience with other specialized software for the above domain will also be considered.

You have graduate‑level expertise (MS or PhD preferred) in the domain listed above, with real hands‑on experience using the specific software tools, not just theoretical knowledge of the field. You’ve written code that calls these libraries to solve actual research problems, understand where they break, what their edge cases are, and what makes a problem genuinely hard versus superficially complex. Beyond domain expertise, the strongest candidates can think like a puzzle designer: constructing problems where the difficulty comes from reasoning strategy rather than brute computation, where multiple plausible approaches exist but only careful analysis leads to the correct one, and where surface‑level pattern matching won’t suffice.

• Graduate‑level training in a relevant STEM domain (MS, PhD, or equivalent research experience).
• Demonstrated proficiency with at least one of the listed scientific software libraries, evidenced by research publications, open‑source contributions, or professional work.
• Strong Python programming skills – you will be writing problem setups, oracle functions, and solution validators.
• Ability to work independently and iterate on problem designs based on calibration 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 pedagogy or exam / problem‑set design.
• Experience with computational reproducibility and containerized environments.