Program Manager - Content Automation Specialist

Role Description

This role focuses on transforming how content quality is validated across AI-powered productivity tools and digital experiences. The Content Automation Specialist will design and implement scalable evaluation systems, automated testing frameworks, and AI-assisted quality methodologies. The objective is to increase operational efficiency while maintaining high standards of human-centered quality. This position plays a key role in advancing content quality practices by:

• Identifying where automation can replace manual review
• Defining where human evaluation remains critical
• Building systems that continuously improve quality at scale

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, Artificial Intelligence, or a related technical field
• Background in test automation, quality engineering, AI evaluation, or systems development
• Experience building testing frameworks, automation workflows, or quality systems
• Demonstrated ability to translate data insights into actionable recommendations
• Strong proficiency in Python and automation tooling
• Experience with AI development and productivity tools such as code editors or AI-assisted platforms
• Strong systems-thinking and technical problem-solving capabilities
• Ability to define scalable processes and frameworks
• Experience working with data to drive decision-making and optimization

Requirements

• Demonstrated stability in prior roles
• Ability to clearly communicate technical concepts and past project experience
• Availability to work standard Pacific Time hours in a remote environment
• Supplier-provided equipment required (standard PC)

Assessment Process

• 1 to 2 interview rounds (30 to 45 minutes each)
• Interviews include technical discussions and review of prior experience
• Candidates are expected to articulate hands-on experience with automation, AI tooling, and quality frameworks

Performance Measurement

• Automation efficiency and measurable reduction in manual effort
• Quality and usability of automation solutions
• Delivery against milestones and timelines
• Effectiveness of recommendations and ability to drive implementation
• Communication of risks, blockers, and progress updates

Unique Selling Points

• Opportunity to shape how AI-generated content is evaluated and improved at scale within advanced digital ecosystems
• Direct impact on automation strategies, quality frameworks, and user experience outcomes
• Exposure to cutting-edge AI tooling, content evaluation systems, and large-scale quality operations
• Influence cross-functional decision-making related to automation, human evaluation, and quality standards
• Work in a highly visible role focused on innovation, efficiency, and scalable impact

Key Responsibilities

• Quality Systems Strategy:
  • Design scalable testing and evaluation frameworks for content, templates, prompt libraries, and AI-generated outputs
  • Define quality measurement approaches and establish testing standards across content ecosystems
  • Recommend optimal balance between automation, AI-driven evaluation, and human review
• Automation and Tooling:
  • Develop automated testing workflows and validation systems
  • Build tools and processes that reduce manual effort while maintaining content integrity
  • Identify opportunities to apply AI for defect detection, content validation, and quality assurance
• Human Evaluation and AI Quality:
  • Design human-in-the-loop evaluation frameworks for AI-generated content
  • Establish quality rubrics, benchmarking criteria, and testing methodologies
  • Determine where human judgment adds measurable value versus scalable automation
• Data and Optimization:
  • Analyze testing metrics and operational data to identify improvement opportunities
  • Provide actionable recommendations to improve quality, efficiency, and coverage
  • Continuously optimize workflows based on performance insights
• Cross-Functional Collaboration:
  • Partner with cross-functional stakeholders to evaluate quality risks and automation opportunities
  • Drive adoption of testing standards and scalable evaluation practices
  • Influence long-term strategies for content quality and AI evaluation