Engineering Simulation Specialist
Mercor · 100% remote · Contract · Posted
- Pay
- $60–90/hr
- Location
- Worldwide
- Languages
- English
- Hours
- Flexible
- Openings
- Not listed
- Level
- Expert
Summary
Write engineering simulations and specification sheets to evaluate AI models, defining specs with pass/fail thresholds and prompts for the model.
What you'll do
- Write engineering simulations in Python
- Define specification sheets with quantitative pass/fail thresholds
- Design prompts for the model to attempt designs
- Ensure simulation problems are rigorous and testable
Requirements
- Have deep applied expertise in control systems, analog or RF circuits, power electronics, or mechanical design
- Hold a PhD or equivalent industry track record
- Write simulations in Python
- Express acceptance criteria as quantitative, testable thresholds
- Design problems that cannot be shortcut but can be judged rigorously
- Work with browser-based studio and GitHub
Skills
- Python
- Control Systems
- Analog Circuits
- RF Circuits
- Power Electronics
- Mechanical Design
- GitHub
Full description
About the role
A frontier engineering-reasoning evaluation run in collaboration with a leading AI research lab. The work measures whether state-of-the-art models can reason from first principles in your engineering domain rather than retrieve facts from training data — and you get visibility into the model's internal reasoning on your own tasks.
What you'll do
Domain experts write simulations and specification sheets; the model attempts to design an artifact — controller gains, circuit parameters, geometry — that satisfies every spec.
You define the simulation, a set of specs with pass/fail thresholds, and the prompt. The model probes your simulation with a limited number of calls, then submits a final design. An agentic grader runs the simulation and scores spec satisfaction.
Who we're looking for
- Deep applied expertise in control systems, analog or RF circuits, power electronics, or mechanical design — PhD or equivalent industry track record.
- Comfortable writing simulations in Python and expressing acceptance criteria as quantitative, testable thresholds.
- Able to design a problem you can't shortcut yourself, but can still judge rigorously.
- Comfortable working in a browser-based studio and GitHub.
Support
Onboarding calls run daily, alongside internal tooling built to help you work faster. Prior model-evaluation experience is not required.
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