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Job Post Details

Physicist
SME Careers by SuperAnnotate
Remote
Up to $70 an hour
Contract
Apply on company site

Job details

Up to $70 an hour
Contract
Remote

Full job description

We’re seeking physicists to join our AI training project. In this role, you will review AI-generated responses, evaluate the reasoning quality and step-by-step problem-solving, and provide expert feedback that helps models produce answers that are accurate, logical, and clearly explained. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in calculations, methodology, or conceptual understanding; fact-check physics information; write high-quality physics explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on physics correctness and reasoning quality.

requirements
  • You hold an MS or PhD in Physics or a related field in one of the top 100 universities.
  • Confident in physical reasoning, mechanics, electromagnetism, optics, thermodynamics/statistical mechanics, and modeling/systems thinking
  • You have developed or critically reviewed complex physics content (e.g., problem banks, proofs, textbook sections, research notes).
  • Expert at fact-checking information using public sources across multiple domains.
  • You communicate physics clearly and turn advanced theory into real-world solutions.
  • Background in research, analytical writing, debate, programming, or physics is preferred.
  • Published in a peer-reviewed journal.
  • English proficiency C1 and above with excellent writing skills.
  • Bonus: Data labeling, RLHF, or AI model evaluation experience.
responsibilities
  • Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
  • Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
  • Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.
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