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Mercor is seeking a researcher to lead frontier engineering‑reasoning evaluations. You will design simulations and specifications, and guide models to produce designs that satisfy all criteria.
The role emphasizes rigorous problem formulation and quantitative thresholds, in a browser‑based studio with GitHub collaboration. The successful candidate will have deep expertise in control systems or related domains, with a PhD or equivalent track record, and familiarity with Python simulations and
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.
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.
Onboarding calls run daily, alongside internal tooling built to help you work faster. Prior model-evaluation experience is not required.