Seeking experienced Portuguese language specialists to lead quality assurance operations across AI training projects, manage contributor quality, and ensure Portuguese content meets linguistic, cultural, and localization standards.
Physics Quality Assurance Lead (QAL)
United States
Up to $75/hr
June 20, 2026
Job description
Role Overview
As a Physics Quality Assurance Lead (QAL), you will oversee quality, consistency, and contributor performance across physics AI training projects.
You will review AI-generated physics content as well as trainer and QA work, evaluate outputs against project guidelines, provide detailed written feedback, and ensure adherence to quality standards.
This role combines scientific expertise, quality management, contributor support, documentation, and process improvement responsibilities within large-scale AI training programs.
Your work will directly contribute to improving some of the world's leading AI models by ensuring physics training data is scientifically accurate, physically sound, clearly explained, and aligned with client expectations.
Key Responsibilities
Quality Monitoring
- Review and spot-check physics training items
- Identify quality issues and recurring patterns
- Provide feedback to contributors
- Escalate critical quality concerns when necessary
Physics Content Review
Review AI-generated:
- Physics explanations
- Calculations
- Derivations
- Diagrams
- Experimental interpretations
- Step-by-step reasoning
Verify:
- Scientific accuracy
- Formula usage
- Unit consistency
- Physical assumptions
- Mathematical correctness
Trainer & QA Communication
Communicate updates regarding:
- Guidelines
- Workflow changes
- Project requirements
- Physics quality expectations
Support contributors through Discord and other communication channels
Question Handling
- Answer questions related to:
- Physical assumptions
- Formula selection
- Units and dimensional analysis
- Derivations
- Experimental setups
- Diagram interpretation
- Rubric application
Contributor Activation
- Contact inactive contributors
- Encourage project participation
- Track follow-ups and contributor availability
Documentation
- Create and maintain:
- Style guides
- FAQs
- Quality trackers
- Onboarding materials
- Calibration tasks
- Quality notes
- Example libraries
- Honeypots
Onboarding & Training
- Conduct onboarding sessions
- Explain:
- Project workflows
- Rubrics
- Quality expectations
- Physics-specific review standards
Quality Alignment
- Ensure consistent application of scientific and physics guidelines
- Communicate project updates and evolving standards
Process Improvement
- Identify recurring quality issues
- Recommend workflow improvements
- Help build scalable physics QA processes
Risk & Accuracy Review
- Flag:
- Physically impossible claims
- Incorrect calculations
- Unsafe scientific guidance
- Misleading explanations
- Poorly contextualized scientific conclusions
Required Qualifications
Bachelor's, Master's, or PhD in:
- Physics
- Applied Physics
- Engineering Physics
- Astrophysics
- Mathematics
- Engineering
- Related quantitative disciplines
Strong English communication skills
Minimum 3 years of experience in:
- Physics research
- Teaching
- Tutoring
- Laboratory work
- Scientific writing
- Academic review
- Engineering analysis
- Related scientific workflows
Physics Expertise
Strong understanding of:
- Classical Mechanics
- Electromagnetism
- Waves
- Optics
- Thermodynamics
- Statistical Mechanics
- Quantum Mechanics
- Relativity
- Dimensional Analysis
- Mathematical Modeling
Evaluation Skills
Ability to identify:
- Incorrect assumptions
- Wrong formulas
- Unit errors
- Sign convention mistakes
- Faulty derivations
- Flawed reasoning
- Physically impossible outcomes
- Misleading explanations
Preferred Experience
Experience with:
- Python
- MATLAB
- Mathematica
- LaTeX
- Data analysis
- Simulations
- Scientific visualization
- Numerical methods
Additional advantages include:
- AI training
- Data annotation
- Scientific QA
- LLM evaluation
- Academic peer review
- Rubric-based assessment
Skills & Technologies
- Physics
- Physics QA
- Scientific Review
- Quantum Mechanics
- Thermodynamics
- Electromagnetism
- Classical Mechanics
- AI Training
- LLM Evaluation
- Trainer Feedback
Selection Process
- AI Interview
- Domain-specific task
- Recruiter interview
Important Note
There may not be an immediate project assignment. Qualified candidates will join the expert network and may be contacted for future opportunities.
Why Work with SME Careers?
Shape the Future of AI
Your work directly impacts how AI systems learn and communicate.
Flexible Work
Choose your own schedule and work remotely.
Weekly Payments
Receive payments once approved work is completed.
Community Rewards
Earn referral bonuses through the SME Careers referral program.
About SME Careers
SME Careers is an AI Data Services company and subsidiary of SuperAnnotate.
The company supports many of the world's largest AI organizations by providing high-quality training data and expert evaluation services for frontier AI systems.
Apply now
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