Physics Expert (Contract)

Physics Expert (Contract)

Labelbox • Remote (United States preferred)
Shape the data that powers frontier AI

Quick facts

• Engagement | Hourly, at‑will contractor
• Schedule | Fully remote & asynchronous (min. 15 hrs/week)
• Pay Range (US) | $25 – $100 per hour
• Start Date | Rolling — staffed as projects launch

What you’ll do

• Data annotation: Accurately label and categorize physics problems, concepts, diagrams, experiments, and other relevant data
• Concept mapping: Connect key physics principles and establish relationships between different topics (e.g., mechanics, electromagnetism, thermodynamics) to help AI models grasp the structure of the subject
• Solution review: Analyze and verify AI-generated solutions to physics problems, identifying errors and offering clear feedback to improve model performance
• Content development: Help create well-rounded training datasets that span a variety of physics topics and difficulty levels

You’re a great fit if

• You bring deep expertise in physics (research, teaching, or applied problem-solving)
• You’ve built or reviewed complex scientific, educational, or technical content (lab work, curriculum, simulations, or similar)
• You communicate challenging physical concepts clearly and enjoy turning theory into real-world understanding
• Bonus: Experience with data labeling, RLHF, or other AI training projects

About the role

• Flexible workload — work from anywhere, on your own schedule
• High impact — your craft directly improves models used by top AI labs & Fortune 500 teams
• Clear ownership — know exactly what success looks like and have autonomy to deliver
• Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs

Interview process

• Complete a screening with Zara, our AI interviewer in English, to learn more about your background and experience.
• Domain-specific Zara interview to assess your understanding of fundamental physics principles, your ability to analyze complex processes, and your skill in applying theoretical frameworks to real-world scenarios.