Physics 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 pipeline 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 ResponsibilitiesIdentify 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 kernelsDevelop full Python solutions for each problem, providing all necessary input files, boundary conditions, and domain or initial condition definitionsEstablish the correct numerical output and define how close the AI model needs to get, using tolerance values appropriate to the physical contextRun 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 environmentsHand off completed tasks to a senior reviewer in your subfield and refine based on their feedback before final submissionCore RequirementsAcademic background in Physics, Theoretical, Experimental, or Computational, or an equivalent fieldAt least 2 years of hands-on experience in physics research, applied work, or teachingSolid Python skills, applied to writing and validating computational solutionsCapacity to build problems that cannot be solved without specialized simulation softwareExcellent written and verbal communication skills in EnglishAbility to work independently in a remote, fast-paced environmentNice-to-HaveWorking 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 independentlyPrior exposure to how frontier AI models approach complex simulation tasksKnowledge spanning more than one physics domain, such as fluid dynamics, electromagnetism, gravitation, or cosmologyFamiliarity with containerized or sandboxed Linux execution environmentsPlease Note: Due to the high volume of applications, only shortlisted candidates will be contacted.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.