In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.
Key Responsibilities
Contribute deep domain expertise in your specialized physics subfield, providing authentic, up-to-date insights for AI training initiatives.
Formulate, solve, and document advanced mathematical physics problems using rigorous methodologies.
Develop and validate computational models, simulations, and analytical solutions leveraging Python, SymPy, and Jupyter environments.
Produce high-quality, publication-level written explanations and derivations with clear, structured reasoning.
Clearly communicate complex concepts and solutions through both written and verbal exchanges with project coordinators and the technical AI team.
Work independently to navigate and resolve challenging, novel research problems aligned with the assigned subfield.
Leverage your experience to review, critique, and improve problem solutions, ensuring accuracy and clarity for AI training.
Required Skills and Qualifications
PhD (or senior PhD student) in physics with active, ongoing research in one or more targeted subfields: High Energy/Mathematical Physics, Biophysics/Statistical Physics, Condensed Matter, AMO/Quantum Optics, Gravitation/Cosmology/Astrophysics, Quantum Information, or Optical properties of materials.
2–5 representative publications (last 5 years) in the relevant subfield, with available arXiv/DOI links.
Expertise in mathematical modeling, derivations, and system proofs at a publication-worthy standard.
Proficient in LaTeX, Python, SymPy, and Jupyter (flag any skill gaps).
Exceptional written and verbal communication skills—clarity, accuracy, and the ability to explain intricate concepts are essential.
Demonstrated ability to work independently and execute research tasks with minimal supervision.
Must be based in the US, UK, or Canada (rare exceptions for truly exceptional candidates).
Preferred Qualifications
Experience in AI training or interdisciplinary computational research projects.
Recognition as a thought leader, contributor, or reviewer in your physics subfield.