Content QA Specialist

Make an Impact!

Are you interested in making a difference in the lives of others?

The McGraw Hill Higher Education team thrives on building meaningful relationships with educators to maximize the potential of every learner.

This is a unique opportunity to have a far-reaching impact on student success as well as impact the educational technology industry.

How can you make an impact?

McGraw Hill, the leading provider of digital and print educational resources, is looking for a Content Quality Assurance Specialist who is responsible for establishing and enforcing quality standards across all AI-generated and human-reviewed content produced within the Curriculum Pod.

This role serves as the critical checkpoint between content production and publication, ensuring that courseware meets instructional, technical, editorial, and compliance standards.

As the organization transitions to AI-powered content development, the Content QA Specialist will adapt quality frameworks to address the unique challenges of AI-generated content, including accuracy verification, academic integrity, pedagogical soundness, and brand alignment.

This is a remote position open to applicants authorized to work for any employer within the United States.

What you will be doing:

• Designed and executed comprehensive QA review processes for AI-generated and human-authored course content, evaluating accuracy, instructional quality, formatting, accessibility, copyright readiness, and compliance; developed and maintained QA rubrics, checklists, and standards documentation aligned with an evolving AI-first content development model.
• Collaborated with the Content Creation Manager to implement quality gates throughout the production pipeline, ensuring defects were identified and resolved prior to publication; maintained and updated quality standards frameworks as AI authoring tools and content delivery mechanisms evolved.
• Reviewed content metadata, tagging structures, and topic hierarchy alignment to verify accurate asset classification within the AI content ecosystem and support content discoverability and organizational standards.
• Partnered with the Content Data Analyst/Researcher to analyze quality trends, identify recurring error patterns, and drive data-informed improvements to production workflows and overall content quality.
• Evaluated AI-generated content for accuracy issues, hallucinations, outdated information, and pedagogical inconsistencies, escalating concerns through appropriate channels; contributed to the continuous improvement of prompt engineering guidelines and AI authoring templates based on QA findings.