Physicist with Python Proficiency - AI Trainer

We’re building a talent pool of Physics Experts with Python proficiency to contribute to project‑based AI development initiatives, focused on evaluating and enhancing frontier AI models. Designed for physics professionals who enjoy deep technical problem solving, this role is for those looking to apply their simulation expertise to evaluate and push the boundaries of frontier AI models, relying on domain‑specific simulation tools such as FEniCS, OpenFOAM, Meep, REBOUND, or CAMB, with verifiable, code‑graded answers run inside isolated Linux environments. Key Responsibilities Identify an appropriate physics simulation package and build problems whose solution genuinely hinges on that tool’s core capabilities, whether PDE solvers, integrators, or Monte Carlo kernels. Develop full Python solutions for each problem, providing all necessary input files, boundary conditions, and domain or initial condition definitions. Establish the correct numerical output and define how close the AI model needs to get, using tolerance values appropriate to the physical context. Run the problem against the AI model across multiple parallel attempts, analyzing where it succeeds or falls short, and adjusting difficulty until the pass rate falls between 10 % and 30 %. Tune solver parameters, field configurations, and initial conditions iteratively, building an understanding of how the model navigates complex simulation environments. Hand off completed tasks to a senior reviewer in your subfield and refine based on their feedback before final submission. Core Requirements Academic background in Physics, Theoretical, Experimental, or Computational, or an equivalent field. At least 2 years of hands‑on experience in physics research, applied work, or teaching. Solid Python skills, applied to writing and validating computational solutions. Capacity to build problems that cannot be solved without specialized simulation software. Excellent written and verbal communication skills in English. Ability to work independently in a remote, fast‑paced environment. Nice‑to‑Have Working knowledge of one or more domain‑specific simulation tools, including but not limited to: FEniCS/DOLFINx, OpenFOAM, Meep, MPB, openEMS, Geant4, PYTHIA8, ROOT/PyROOT, WarpX, REBOUND, MESA, CAMB, CLASS, or Bilby, or a demonstrated ability to get up to speed independently. Prior exposure to how frontier AI models approach complex simulation tasks. Knowledge spanning more than one physics domain, such as fluid dynamics, electromagnetism, gravitation, or cosmology. Familiarity with containerized or sandboxed Linux execution environments. #J-18808-Ljbffr Kake