Physics Quality Assurance Lead

Role overview

This is a remote contractor position paid on an hourly basis. You will lead physics quality assurance efforts across AI training initiatives, with a focus on improving the consistency, accuracy, and reliability of both trainer output and QA review. Your work will help ensure that physics-related training data meets client expectations and supports the development of high-performing AI models.

The organization is an AI data services company that supplies training data to major AI companies and foundation-model labs. There is not an active project available immediately for this position; however, suitable candidates may be contacted first when matching opportunities become available. Joining the expert network also gives access to future projects.

What the role involves

You will evaluate AI-generated physics content and review the work of trainers and quality reviewers against project-specific rules. The role requires close attention to scientific accuracy, physical reasoning, calculation correctness, unit consistency, formula usage, conceptual clarity, experimental understanding, formatting, instruction adherence, and rubric compliance. You will also identify patterns in quality problems, share updates with contributors, support onboarding, maintain documentation, and help re-engage contributors who are inactive.

Selection process

The hiring process includes an AI interview, a subject-matter task, and a conversation with a recruiter.

Eligibility

• Applicants should hold a bachelor’s, master’s, or PhD degree in Physics, Applied Physics, Engineering Physics, Astrophysics, Mathematics, Engineering, or a closely related quantitative or scientific discipline.
• Strong written and spoken English is needed to understand instructions, coordinate with teams, and deliver precise technical feedback.
• A minimum of 3 years’ experience in physics research, teaching, tutoring, laboratory work, science writing, academic review, engineering analysis, or similar scientific work is expected.
• Candidates should be comfortable handling physics content review, rubric-based evaluation, and scientific quality control.
• Experience with remote collaboration tools and structured documentation is important.

Responsibilities

• Review physics questions and responses, spot quality concerns, and send detailed feedback while escalating serious or repeated issues.
• Check AI-produced explanations, calculations, diagrams, derivations, experimental interpretations, and step-by-step solutions.
• Keep trainers and QAs informed about updated instructions, workflow changes, and physics-specific quality standards through Discord.
• Answer questions about assumptions, formulas, units, derivations, diagrams, experimental setups, and rubric interpretation.
• Reach out to inactive contributors, encourage participation, monitor follow-ups, and note availability concerns.
• Build and maintain physics-related documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding resources.
• Lead onboarding and training sessions for physics contributors.
• Identify inaccurate, misleading, numerically wrong, physically impossible, unsafe, or poorly contextualized claims.
• Track recurring issues and help improve physics QA processes and workflows.

Requirements

• Deep understanding of classical mechanics, electromagnetism, waves, optics, thermodynamics, statistical mechanics, quantum mechanics, relativity, units, dimensional analysis, and mathematical modeling.
• Ability to judge physics content against rubrics and detect weak assumptions, incorrect formulas, unit mistakes, flawed reasoning, sign errors, impossible statements, or unclear explanations.
• Preferred familiarity with Python, MATLAB, Mathematica, LaTeX, laboratory methods, data analysis, simulations, scientific visualization, and numerical techniques.
• Strong preference for people who have led or supported remote teams of educators, reviewers, researchers, annotators, science writers, or QAs.
• Comfort using Discord, Google Sheets, Google Docs, trackers, dashboards, and project management tools.
• Excellent organization skills for maintaining style guides, FAQs, trackers, onboarding content, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-driven review is a strong advantage.

Additional information

This is an hourly remote contract role rather than a fixed long-term staff opening. The position is part of a growing AI data services business supporting some of the world’s largest AI organizations. The work has a direct impact on improving physics-focused training data so that it is accurate, scientifically sound, clearly explained, properly documented, and aligned with client needs.

Terms and conditions

No immediate project is available at this time. Qualified candidates may be contacted when relevant work opens up, and placement into the expert network may lead to future project opportunities.