Physics AI Trainer
Weekday AI
Physics AI Trainer
Weekday AI
Proactive software engineer recruitment via referrals
Weekday AI · United States (Remote) · Part-time · Posted 2025-09-09
Workplace: remote
Department: Weekday's Client via platform
This role for one of Weekday's clients.
We are currently helping hire for one of the leading AI labs (via one of our partners); helping them train their AI models.
Have you been curious as to how LLMs generate expert answers and would you like to contribute to their development? There are a bunch of gaps in the understanding of these LLMs in certain key specialised areas (like physics). We require your help in building these models much better (and thereby expansion of human understanding of these fields).
What exactly would do?
You’d be guiding research teams to close knowledge gaps and improve AI model performance in Physics domain.
You will be evaluating advanced math tasks and solutions and provide clear, written feedback
Requirements:
🎓 Education: PhD in Physics from a top tier university
💼 Work experience: 2+ years of academic experience (research/teaching) or professional experience (scientist, engineer or researcher)
📍 Location: Must be currently based out of the US
💵 Compensation: $70 to $85 per hour
🕒 Time commitment: 20 hours per week commitment (can go to ~40)
⏳ Project duration: 4-8 weeks
Flexible, remote work that will have a significant impact on the development of advanced AI models
Async work and communication
Work type: Part-time
Industry: Internet and Artificial Intelligence
You would not be employed full-time and will be paid as an independent contractor.
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Proactive software engineer recruitment via referrals
Weekday.works helps companies hire software engineers through proactive outreach and peer-recommended candidates. Its platform automates tracking, pipeline management, and provides weekly updates, while it actively identifies engineers via peer networks, reaches out, shortlists, checks references, and schedules interviews. Differentiators include peer-backed vetting instead of traditional job boards, proactive outreach, and a passive-income reference feature that monetizes referrals. The goal is to reduce the time and effort of hiring by connecting companies with pre-vetted engineers who are open to new opportunities through a network-driven approach.
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