Your deep understanding of physics will directly shape how AI reasons through complex problems — from undergraduate fundamentals to Masters-level challenges.
If you've spent years thinking carefully about mechanics, electromagnetism, or thermodynamics, this is your opportunity to apply that expertise in a cutting-edge field — fully remote, on your own schedule.
Why Join Us
Work directly with industry-leading AI labs on cutting-edge model development.
Fully remote and flexible — work when and where it suits you.
High agency and autonomy — you manage your own time and workload.
Contribute to meaningful work that shapes how AI understands science.
Potential for ongoing contracts and expanded project involvement.
Global collaboration with a network of expert contributors.
Responsibilities
Design rigorous, advanced physics problems across domains including mechanics, electromagnetism, thermodynamics, and more.
Develop clear, step-by-step solutions with precise, logical reasoning.
Evaluate AI-generated outputs for scientific accuracy and quality of reasoning.
Collaborate with researchers to build and refine benchmarks spanning undergraduate to Masters-level physics.
Provide structured written feedback to help improve model performance.
Required skills
Strong analytical and problem-solving skills across multiple advanced physics domains.
Able to communicate complex concepts clearly and systematically in writing.
Self-motivated and comfortable working independently and asynchronously.
Preferred skills
Experience with data annotation, data quality review, or evaluation workflows.
Familiarity with AI or large language model (LLM) research.
Background in academic tutoring, curriculum design, or scientific writing.
Experience and education
Pursuing or holding a Master's degree in Physics, Applied Physics, or a closely related field.
No prior AI experience required — your physics knowledge is what matters.
Thriveth makes AI data-training work easier to find, understand, and navigate. We replace uncertainty with clear opportunities, realistic expectations, and insights from real application journeys.
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