Physics Expert (Mexico)

What You'll Do5

• Design physics problems and scenarios that reflect real simulation, modeling, and computational practice.
• Assess AI-generated answers for technical accuracy, numerical rigor, and fidelity to actual physics workflows.
• Write structured feedback that points out errors, gaps, and reasoning flaws in model outputs.
• Apply your subfield expertise to test how well AI handles concepts from condensed matter, astrophysics, high-energy physics, or fluid dynamics.
• Work at your own pace without a required schedule or minimum hour count.

Requirements11

• PhD in physics with a computational or theoretical focus.
• First-author publications in a computational subfield or at least two years of hands-on industry experience.
• Depth in numerical simulation, PDE or ODE solvers, and Monte Carlo methods.
• Background in HPC and scientific computing with NumPy, SciPy, C++, or Fortran.
• Expertise in a core subfield such as condensed matter, astrophysics, high-energy physics, or fluid dynamics.
• Mexican work authorization.
• Strong written communication and close attention to detail.
• Comfort explaining complex physics concepts in plain language.
• Ability to work without direct supervision in a remote, asynchronous setting.
• Experience running large-scale simulations on HPC clusters across multiple scientific computing environments.
• Familiarity with lattice methods or other specialized techniques in your subfield.

Who Should Apply

Physicists who hold a PhD with computational or theoretical training and can point to first-author papers or 2+ years of industry work fit this project. The strongest applicants bring depth in numerical simulation, PDE/ODE solvers, Monte Carlo methods, or a core subfield like condensed matter or astrophysics, plus comfort with HPC tools such as NumPy, SciPy, C++, or Fortran. The role is less suited to physicists without Mexican work authorization, or those who prefer a fixed schedule and close team oversight. Candidates often score low when they list physics knowledge but cannot show concrete simulation, modeling, or coding work. Another common miss is weak written feedback: this project depends on clear, structured critiques of AI output, not just correct final answers.

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