UX Researcher - AI

📍 Job Overview

Job Title: UX Researcher - AI

Company: Red River

Location: Raleigh, North Carolina, United States

Job Type: FULL_TIME

Category: User Experience Research / AI Program Management

Date Posted: May 1, 2026

Experience Level: 2-5 Years

Remote Status: Hybrid

🚀 Role Summary

• This unique role bridges hands-on UX research with program-level AI tooling development and knowledge management for a UX Research Program.

• Responsibilities include contributing to and maintaining AI-driven UX research tools, evaluating AI output quality, and managing participant recruitment infrastructure.

• The position requires strong analytical skills for querying participant databases (SQL, R, Python) and a keen eye for detail in maintaining team standards and best practices.

• Opportunity to conduct primary research and leverage existing data sources to uncover user insights, while also building and maintaining a structured research insights repository.

📝 Enhancement Note: This role is a hybrid of individual contributor research work and program management focused on AI integration and operational efficiency within a UX research function. The emphasis on AI tooling and program-level support suggests a need for candidates who can think strategically about scaling research operations.

📈 Primary Responsibilities

• Develop and contribute to a shared repository of AI-driven UX research tools designed to augment and scale research workflows for embedded researchers.

• Evaluate the quality of AI-generated research artifacts, assessing them for accuracy, tone, bias, and interpretive validity to ensure research integrity.

• Query and analyze existing participant databases using SQL, R, or Python to identify and address sampling biases, demographic gaps, and representation issues.

• Manage and enhance recruitment infrastructure, including participant panels, scheduling workflows, relevant tooling, and standardized templates.

• Maintain and evolve the team’s standards and best practice documentation for UX research processes and AI tool utilization.

• Partner with Senior Researchers to build and maintain a structured research insights repository, ensuring knowledge is accessible and actionable.

• Conduct primary research for ongoing programs, such as benchmarking studies, UX measurement initiatives, and continuous user interviews.

• Analyze existing data sources, including survey backlogs, customer feedback, support tickets, and prior study findings, to surface patterns and generate new insights.

📝 Enhancement Note: The responsibilities highlight a dual focus: operationalizing research processes through AI and infrastructure improvements, and contributing directly to research insights through primary and secondary data analysis. This requires a candidate comfortable with both strategic program development and tactical research execution.

🎓 Skills & Qualifications

Education:

Experience:

• 2+ years of experience conducting mixed-methods UX research, encompassing both qualitative and quantitative approaches.

• Experience in participant management, including recruitment infrastructure, tooling, templates, or program coordination.

• Proven ability to review and provide constructive feedback on research plans, guides, and deliverables.

Required Skills:

• Strong foundational knowledge of UX research methods (qualitative and quantitative), even if not conducting studies daily.

• Proficiency in querying and analyzing data using SQL, R, or Python for participant database management.

• Exceptional writing and documentation skills, with the ability to translate complex methodologies into clear, enforceable criteria.

• Strong communication skills, with the ability to train, advise, and influence researchers and cross-functional partners.

• High attention to detail and a quality mindset, with the ability to identify flaws in research instruments and deliverables.

Preferred Skills:

• Experience building or maintaining a research repository or knowledge management system.

• Experience developing research training programs or onboarding materials for research teams.

• Exposure to AI/LLM-based research tools or a strong interest in how automation can support research workflows.

• Experience working in enterprise or B2B product environments.

📝 Enhancement Note: The requirements emphasize a blend of methodological rigor in UX research and practical skills in data management, AI tool integration, and program operations. The "Plus" section indicates a strong preference for candidates with experience in knowledge management systems, training, and AI/LLM exposure, suggesting these are key areas for program growth.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase examples of research plans, methodologies, and deliverables that demonstrate a strong understanding of mixed-methods research design.

• Include case studies illustrating how you have managed participant recruitment, panels, or scheduling workflows, highlighting efficiency improvements.

• Provide examples of documentation you have created or maintained for research standards, best practices, or knowledge management systems.

Process Documentation:

• Documented workflows or processes related to research operations, such as participant recruitment, data analysis pipelines, or insights repository management.

• Examples of how you have improved or standardized research processes, emphasizing efficiency and scalability.

• Evidence of contributing to or maintaining knowledge management systems, including taxonomies and best practice guides.

📝 Enhancement Note: While not explicitly stated as a formal "portfolio review," the job description implies that candidates should be prepared to demonstrate their experience through examples. A strong candidate will be able to articulate their contributions to process improvement, documentation, and the operational aspects of UX research.

💵 Compensation & Benefits

Salary Range: $96,440.00 - $154,190.00 annually. Note: Actual compensation will be based on qualifications, experience, job location, and other factors.

Benefits:

• Comprehensive medical, dental, and vision coverage.

• Flexible Spending Account (healthcare and dependent care).

• Health Savings Account (for high-deductible medical plans).

• Retirement 401(k) with employer match.

• Generous paid time off and holidays.

• Paid parental leave plans for all new parents.

• Various leave benefits, including disability, paid family medical leave, and paid military leave.

Working Hours:

• Standard full-time work schedule, likely around 40 hours per week. The hybrid nature suggests a balance between in-office and remote work, offering flexibility.

📝 Enhancement Note: The provided salary range is a key indicator of the role's seniority and scope. The extensive benefits package, typical of a large enterprise like Red Hat, underscores the company's commitment to employee well-being and professional development. The "Pay Transparency" note indicates that the final offer is highly individualized.

🎯 Team & Company Context

🏢 Company Culture

Industry: Enterprise Open Source Software Solutions. Red Hat is a leader in providing Linux, cloud, container, and Kubernetes technologies.

Company Size: Over 10,000 employees globally.

Founded: 1993. Red Hat has a long history in the open-source community, fostering a culture of innovation, collaboration, and community-driven development.

Team Structure:

• The role is part of the "UX Research Program and Practice team."

• This team likely operates across various product development groups, supporting embedded UX researchers.

• The structure suggests a central function that provides resources, tools, standards, and expertise to decentralized research efforts.

Methodology:

• Adheres to open-source principles: transparency, collaboration, community-driven development.

• Emphasis on data-driven decision-making and leveraging technology (AI) to enhance processes.

• Focus on quality, accuracy, and methodological rigor in research.

• Values inclusive environments where diverse perspectives contribute to innovation.

Company Website: https://www.redhat.com/

📝 Enhancement Note: Red Hat's strong open-source foundation influences its culture, promoting transparency and collaboration. The company's focus on enterprise technology and Kubernetes suggests a fast-paced, innovation-driven environment. The "UX Research Program and Practice team" indicates a mature organization with dedicated resources for UX research operations and strategy.

📈 Career & Growth Analysis

Operations Career Level: This role appears to be at an intermediate level, suitable for someone with 2-5 years of experience. It combines hands-on research with program management and operational responsibilities, offering a pathway beyond pure individual contributor roles.

Reporting Structure: The role reports into the "UX Research Program and Practice team" and will partner with a "Senior Researcher." This suggests a hierarchical structure within the UX research function, with opportunities to learn from more experienced researchers and program managers.

Operations Impact: The role directly impacts operational efficiency and quality within the UX research function by developing AI tools, managing infrastructure, and standardizing best practices. This allows embedded researchers to be more effective, ultimately influencing product development and user experience across Red Hat's enterprise offerings.

Growth Opportunities:

• Specialization: Deepen expertise in AI/LLM applications for research, research operations, or program management.

• Leadership: Progress to a Senior UX Researcher or Research Program Manager role, leading initiatives and managing teams.

• Skill Development: Gain exposure to enterprise-level product development, cross-functional collaboration, and advanced data analysis techniques.

• Industry Exposure: Contribute to the evolving field of AI in UX research, potentially presenting findings or best practices internally or at conferences.

📝 Enhancement Note: This role offers a unique growth trajectory for a UX researcher who is interested in the operational and programmatic aspects of research, particularly through the lens of AI and automation. It provides a bridge from direct research execution to scaling research capabilities within a large enterprise.

🌐 Work Environment

Office Type: Hybrid work model. This implies a blend of working remotely and from the Red Hat office in Raleigh, NC.

Office Location(s): Raleigh, North Carolina, United States.

Workspace Context:

• The environment is likely collaborative, given Red Hat's open-source culture.

• Expect access to standard enterprise software and tools, with a specific focus on AI and data analysis platforms.

• Opportunities for interaction with a diverse team of researchers, engineers, and product professionals.

Work Schedule:

• Primarily a standard 40-hour work week.

• Flexibility is implied through the hybrid model, allowing for a balance between personal and professional commitments.

📝 Enhancement Note: The hybrid nature of the role suggests a need for self-discipline and effective time management. The emphasis on collaboration within an open-source culture indicates an environment where proactive communication and knowledge sharing are valued.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Likely a recruiter screen to assess basic qualifications, experience, and cultural fit.

• Hiring Manager Interview: Deeper dive into your experience with UX research methodologies, AI tool integration, program management, and your understanding of Red Hat's context. Expect questions about specific projects and challenges.

• Team/Peer Interviews: Conversations with embedded UX researchers or members of the program team. Focus will be on collaboration style, problem-solving approach, and how you would contribute to the team's goals.

• Potential Skills Assessment/Case Study: You may be asked to present on a past project, discuss how you would approach a specific research operational challenge, or analyze a hypothetical scenario related to AI tool evaluation or participant database analysis.

• Final Interview: May involve senior leadership to discuss strategic alignment and overall fit.

Portfolio Review Tips:

• Highlight Hybrid Skills: Showcase projects demonstrating both hands-on research (methodology, insights) and operational contributions (process improvement, documentation, tool evaluation).

• Quantify Impact: Whenever possible, use metrics to demonstrate the efficiency gains, quality improvements, or insights generated through your work.

• Focus on Process & Systems: Include examples of how you've managed or improved research processes, built repositories, or evaluated tools.

• AI/Data Analysis Examples: If you have experience with SQL, R, Python for data analysis, or have evaluated AI/LLM tools, prepare specific examples.

• Documentation Samples: Be ready to share examples of research plans, best practice guides, or repository structures you've contributed to.

Challenge Preparation:

• Research Operations Scenarios: Prepare to discuss how you would address challenges like participant bias, scaling research efforts, or ensuring the quality of AI-generated research outputs.

• Methodological Depth: Be ready to articulate the strengths and weaknesses of various UX research methods and when to apply them.

• AI Tool Evaluation: Think about criteria for evaluating AI tools for research, including accuracy, bias, cost-effectiveness, and integration feasibility.

• Communication & Influence: Practice articulating complex ideas clearly and persuasively, as you'll need to train and advise researchers.

📝 Enhancement Note: While a formal portfolio submission might not be explicitly requested, candidates should prepare to "show, don't just tell." The interview process will likely assess practical application of skills and strategic thinking about research operations and AI integration.

🛠 Tools & Technology Stack

Primary Tools:

• Data Analysis & Querying: SQL, R, Python (for participant database analysis).

• AI/LLM Tools: Exposure to or experience with AI-driven research tools (specifics not detailed, but the role is about developing/evaluating them).

• Productivity Suites: Likely standard office suite tools for documentation and communication.

Analytics & Reporting:

• Tools for managing and analyzing survey data, customer feedback, and support tickets.

• Potential use of UX benchmarking and measurement tools.

CRM & Automation:

• Experience with participant management systems or CRM functionalities related to user research panels.

• Understanding of workflow automation principles, particularly as applied to research processes.

📝 Enhancement Note: Proficiency in SQL, R, or Python for data manipulation and analysis is a critical technical requirement. Familiarity with AI/LLM tools, or at least a strong aptitude for learning and evaluating them, is essential given the role's focus. The ability to work with various data sources and contribute to knowledge management systems is also key.

👥 Team Culture & Values

Operations Values:

• Quality & Rigor: A strong commitment to maintaining high standards in research methodology, data analysis, and deliverables.

• Efficiency & Scalability: Driving improvements in research workflows and infrastructure, particularly through AI and automation, to support a growing organization.

• Collaboration & Community: Embracing the open-source spirit of working together, sharing knowledge, and supporting fellow researchers.

• Data-Driven Insights: Utilizing data to inform decisions, identify patterns, and uncover user needs.

• Continuous Learning: Staying abreast of advancements in UX research, AI, and related technologies.

Collaboration Style:

• Cross-functional Integration: Working closely with embedded UX researchers, AI developers, product managers, and other stakeholders to ensure research operations meet diverse needs.

• Knowledge Sharing: Actively contributing to and utilizing team documentation, repositories, and best practice guides.

• Mentorship & Support: Providing guidance and training to other researchers on AI tools, research standards, and best practices.

• Feedback Culture: Open to giving and receiving constructive feedback to continuously improve processes and research quality.

📝 Enhancement Note: Red Hat's culture is deeply rooted in open-source principles, which translates to valuing transparency, collaboration, and collective problem-solving. For this role, it means being comfortable sharing work, contributing to shared resources, and working effectively across different teams and expertise areas.

⚡ Challenges & Growth Opportunities

Challenges:

• Balancing Programmatic and Hands-on Work: Effectively managing time and priorities between developing AI tools/program infrastructure and conducting direct research.

• Evaluating AI Efficacy and Bias: Developing robust methods to assess the accuracy, fairness, and validity of AI-generated research outputs.

• Scaling Research Operations: Implementing solutions that can support a growing number of researchers and product teams efficiently.

• Integrating New Technologies: Staying ahead of the curve with rapidly evolving AI technologies and understanding their practical application in UX research.

• Bridging Technical and Research Skills: Effectively communicating technical concepts related to AI and data analysis to a research audience, and vice-versa.

Learning & Development Opportunities:

• AI in Research Specialization: Become an expert in applying AI and LLMs to enhance UX research processes.

• Research Operations Leadership: Develop skills in managing research programs, infrastructure, and knowledge management systems.

• Advanced Data Analytics: Enhance proficiency in SQL, R, and Python for complex data analysis and participant database management.

• Cross-functional Experience: Gain exposure to enterprise software development cycles, cloud technologies, and Kubernetes ecosystems.

📝 Enhancement Note: This role presents a significant opportunity for growth in a cutting-edge area where AI is transforming research practices. Candidates who embrace challenges related to technology adoption and process optimization will find ample room for development.

💡 Interview Preparation

Strategy Questions:

• "How would you approach evaluating the quality and potential biases of AI-generated research summaries or insights?"

• "Describe a time you improved a research process or implemented a new system to increase efficiency. What was the outcome?"

• "Imagine you need to identify and recruit participants for a niche B2B enterprise software user group. What steps would you take, and what tools might you use?"

• "How would you balance your time between developing AI tooling for the research team and conducting hands-on user research?"

Company & Culture Questions:

• "What interests you about Red Hat and our approach to open source?"

• "How do you see AI impacting the future of UX research?"

• "Describe your experience working in a collaborative, cross-functional environment."

Portfolio Presentation Strategy:

• Structure: Organize your examples by themes: (1) Mixed-Methods Research Execution, (2) Research Operations & Process Improvement, (3) Data Analysis & Insights Generation, (4) Documentation & Knowledge Management.

• Quantify: For each example, clearly state the problem, your approach, the tools/methods used, your specific contributions, and the measurable outcomes or impact.

• Highlight AI/Data Skills: Specifically call out any projects where you used SQL, R, Python, or evaluated AI tools.

• Tell a Story: Frame your contributions as solutions to problems, demonstrating your strategic thinking and problem-solving abilities.

📝 Enhancement Note: Prepare to discuss both your research craft and your operational mindset. Demonstrating an understanding of how to scale research through technology and process is crucial for this role. Articulating your thought process for AI evaluation and data analysis will be key.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Red Hat careers portal via the provided link.

• Tailor Your Resume: Highlight experience relevant to mixed-methods UX research, AI tooling evaluation, data analysis (SQL, R, Python), participant management, and documentation/knowledge management. Use keywords from the job description.

• Prepare Your Portfolio: Gather examples of research plans, deliverables, process documentation, and any case studies demonstrating your ability to improve research operations or derive insights from data. Be ready to discuss these in detail.

• Research Red Hat: Understand their products, their open-source philosophy, and their commitment to innovation. This will help you tailor your answers and demonstrate genuine interest.

• Practice Interview Responses: Prepare to answer behavioral and situational questions related to your experience with research methodologies, problem-solving, collaboration, and your approach to AI and process improvement.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

---