Physics Quality Assurance Lead (QAL)

SME Careers; Himachal Pradesh; Remote; INR 1,102,000 - 1,653,000; Part time

Job summary

SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, seeks a Physics Quality Assurance Lead to oversee QA across physics AI training projects. You will review AI-generated physics content, provide precise feedback, and ensure adherence to rubrics and guidelines.

You will monitor correctness, unit consistency, and experimental interpretations, while coordinating with trainers and QAs via Discord and documentation updates.

Qualifications

• Bachelor's, Master's, or PhD in a quantitative or scientific field.
• Strong command of English for technical feedback and guidelines.
• 3+ years in physics research, teaching, tutoring, or related workflows.
• Ability to evaluate content against rubrics and identify issues.
• Familiarity with Python, MATLAB, Mathematica, LaTeX or similar tools preferred.
• Experience leading or supporting remote education/review teams is preferred.

Responsibilities

• Spot-check physics items and provide feedback via direct messages.
• Review AI-generated explanations, calculations, diagrams, and reasoning.
• Update trainers and QAs on guidelines and quality expectations.
• Respond to questions on physical assumptions, formulas, and units.
• DM inactive contributors, track follow-ups, and flag availability issues.
• Create and maintain physics documentation, style guides, and onboarding materials.
• Run onboarding/training calls for physics contributors.
• Flag misleading or incorrect physics claims and improve QA workflows.
• Identify recurring quality gaps and improve QA processes.

Skills

Physics

Rubrics interpretation

Technical feedback

Remote team leadership

English communication

DiSCussion facilitation

Education

Bachelor's in Physics

Master's in Physics

PhD in Physics

Tools

Python

MATLAB

Mathematica

LaTeX

Job description

In this hourly, remote contractor role, you will work as a Physics Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across physics AI training projects. You will review AI-generated physics content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected quality standards.

You will assess work for scientific accuracy, physical reasoning, calculation correctness, unit consistency, formula use, conceptual clarity, experimental understanding, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your physics quality leadership will directly help improve the world’s premier AI models by ensuring that physics training data is accurate, physically sound, clearly explained, well-documented, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Key Responsibilities

• Spot-check physics items, identify quality issues, provide feedback through DMs, and elevate recurring or critical issues.
• Review AI-generated physics explanations, calculations, diagrams, derivations, experimental interpretations, and step-by-step reasoning.
• Update trainers/QAs on Discord about guidelines, workflow updates, and physics-specific quality expectations.
• Respond to questions around physical assumptions, formulas, units, derivations, diagrams, experimental setups, and rubric interpretation.
• DM inactive contributors, encourage activation, track follow-ups, and flag availability issues.
• Create and maintain physics documentation, style guides, trackers, FAQs, examples, honeypots, and onboarding materials.
• Run onboarding/training calls for physics contributors.
• Flag misleading, numerically incorrect, physically impossible, unsafe, or poorly contextualized physics claims.
• Identify recurring quality gaps and improve physics QA workflows.

Your Profile

• Bachelor's, Master's, or PhD degree in Physics, Applied Physics, Engineering Physics, Astrophysics, Mathematics, Engineering, or a closely related quantitative/scientific field.
• Strong grasp of the English language to follow guidelines, communicate with teams, and provide clear technical feedback.
• 3+ years of experience in physics research, teaching, tutoring, laboratory work, science writing, academic review, engineering analysis, or related scientific workflows.
• Strong understanding of classical mechanics, electromagnetism, waves, optics, thermodynamics, statistical mechanics, quantum mechanics, relativity, units, dimensional analysis, and mathematical modeling.
• Ability to evaluate physics content against rubrics and identify issues such as incorrect assumptions, wrong formulas, unit errors, flawed reasoning, sign convention mistakes, physically impossible claims, or misleading explanations.
• Familiarity with tools or methods such as Python, MATLAB, Mathematica, LaTeX, laboratory methods, data analysis, simulations, scientific visualization, and numerical methods is preferred.
• Experience leading or supporting remote teams of educators, reviewers, researchers, annotators, science writers, or QAs is strongly preferred.
• Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly organized and able to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and documentation.
• Experience with AI training, data annotation, LLM evaluation, scientific QA, academic review, or rubric-based review is a strong plus.
• Native fluency in Punjabi