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 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 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.

Please Note: Due to the high volume of applications, only shortlisted candidates will be contacted.

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 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.

Please Note: Due to the high volume of applications, only shortlisted candidates will be contacted.