Senior UX Researcher AI

📍 Job Overview

Job Title: Senior UX Researcher AI

Company: Red River

Location: Raleigh, North Carolina, United States

Job Type: FULL_TIME

Category: UX Research / Data Science / AI

Date Posted: May 01, 2026

Experience Level: 5-10 Years

Remote Status: Hybrid

🚀 Role Summary

• This Senior UX Researcher role is unique, operating at the intersection of user research, data science, and AI-augmented research, focusing on AI research tooling development and evaluation to scale UX workflows.

• You will be instrumental in building and maintaining a structured, machine-readable research repository, transforming qualitative and quantitative data into queryable units for downstream systems.

• The position requires strong analytical skills in querying and analyzing participant databases using SQL, R, or Python to identify and address sampling biases and recruitment gaps.

• Responsibilities include conducting mixed-methods research, leveraging existing data sources for insights, and designing/executing UX benchmarking studies for longitudinal measurement.

📝 Enhancement Note: This role is positioned as a specialized UX Research function, heavily leaning into data science and AI capabilities. It diverges from traditional UX research by emphasizing the development and application of AI tools, data infrastructure, and advanced analytics to enhance research operations and output. The hybrid nature suggests a blend of in-office collaboration and remote flexibility, typical for roles requiring both deep focus and team interaction.

📈 Primary Responsibilities

• Develop, evaluate, and iterate on a shared repository of AI-driven UX research tools designed to augment and scale the research workflow, integrating them into the software development lifecycle (SDLC).

• Design and implement evaluation rubrics for AI output quality, assessing accuracy, tone, bias, and interpretive validity, iterating on tools based on measurable performance criteria.

• Analyze existing participant databases using SQL, R, or Python to identify sampling biases, demographic gaps, and representation issues, and develop data-driven recruitment strategies to address these.

• Design and validate surveys and screening instruments to qualify participants, ensuring accurate reporting of findings using appropriate statistical methods.

• Transform qualitative and quantitative research data into structured, atomic units for cross-system consumption, defining and maintaining schema and taxonomy to make findings machine-readable and queryable.

• Conduct mixed-methods research by analyzing secondary data sources (surveys, feedback, tickets, prior studies) and designing/executing UX benchmarking studies with standardized instruments.

• Pair qualitative findings with behavioral analytics or benchmark data to triangulate insights and strengthen evidence for product development decisions.

• Ensure data quality and consistency standards across the research repository and contribute to prompt engineering and evaluation frameworks for AI output.

📝 Enhancement Note: The responsibilities highlight a deep dive into operationalizing research data and leveraging AI. The emphasis on "atomic, structured units" and "machine-readable" data points to building a data infrastructure for research, which is a key aspect of advanced operations roles. The integration into SDLC and focus on benchmarking also indicate a strategic, process-oriented approach to UX research.

🎓 Skills & Qualifications

Education:

Experience:

• 5+ years of experience conducting mixed-methods UX research (qualitative and quantitative) within an enterprise product development environment.

Required Skills:

• AI/LLM Tooling: Experience developing, evaluating, and using AI/LLM-based tools within a research context, with a strong understanding of reliability and failure modes.

• Data Analysis & Querying: Proficiency in querying and analyzing large datasets using SQL, R, or Python.

• Statistical Analysis: Strong fluency in statistical analysis, including the ability to select and apply appropriate methods for different data types and report findings with confidence.

• Survey Design & Validation: Expertise in survey and screener design, with a keen attention to sampling validity.

• Research Tools: Proficiency with survey platforms such as Qualtrics.

• Research Repository Management: Experience building or contributing to research repositories, taxonomies, or knowledge management systems.

• Research Ethics: Solid understanding of research ethics, including participant privacy, secure data handling, and bias mitigation strategies.

• Secondary Analysis: Proven experience with secondary analysis, effectively synthesizing findings from multiple existing studies, surveys, or feedback channels.

Preferred Skills:

• Software Development Collaboration: Comfort working within code repositories (e.g., Git) and collaborating with engineering teams using tools like Markdown and CLI.

• Prompt Engineering: Experience with prompt engineering, evaluation frameworks, or AI output quality assessment.

• Qualitative Frameworks: Familiarity with frameworks like Jobs to Be Done, mental models, or similar approaches for structuring qualitative insights.

• UX Metrics Programs: Experience with defining KPIs, tracking longitudinal benchmarks, and reporting UX metrics to stakeholders.

• RAG Pipelines: Foundational understanding of Retrieval-Augmented Generation (RAG) pipelines, including vector stores, chunking strategies, and embedding models.

📝 Enhancement Note: The strong emphasis on programming languages (SQL, R, Python) and AI/LLM tools, alongside traditional UX research skills, indicates a hybrid role that bridges research methodology with data engineering and AI development. The preference for candidates comfortable with code repositories and RAG pipelines suggests an advanced technical aptitude is highly valued.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• AI Tooling Impact: Showcase examples of how AI or advanced analytical tools were used to enhance research efficiency, scale, or insight generation, detailing the problem, solution, and measurable outcomes.

• Data Infrastructure Projects: Include projects demonstrating experience in transforming raw research data into structured, queryable formats, such as contributions to research repositories, taxonomies, or knowledge management systems.

• Data Analysis & Reporting: Present case studies where SQL, R, or Python were used for complex data analysis, recruitment strategy development, or identifying systemic issues in participant data, clearly outlining the analytical approach and findings.

• Mixed-Methods Integration: Provide examples of research projects that effectively combined qualitative and quantitative methods, particularly those that triangulated findings with benchmark data or behavioral analytics.

Process Documentation:

• Workflow Optimization: Demonstrate an understanding of how to document and optimize research workflows, particularly those involving AI augmentation, data structuring, or automated analysis.

• System Integration: Illustrate experience in integrating research tools or data outputs into broader systems, such as the software development lifecycle (SDLC) or cross-functional dashboards.

• Measurement & Reporting: Showcase examples of establishing and tracking UX metrics, conducting benchmarking studies, and reporting on key performance indicators to stakeholders.

📝 Enhancement Note: The portfolio requirements are highly specific, demanding evidence of experience with AI, data infrastructure, and advanced analytics. Candidates should be prepared to demonstrate not just research execution, but also the operationalization of research data and the development/application of novel research tooling. This aligns with the need for a "machine-readable" repository and AI-augmented workflows.

💵 Compensation & Benefits

Salary Range: $118,600.00 - $195,680.00 annually. The final offer will be determined based on individual qualifications, experience, and market value.

Benefits:

• Health & Wellness: Comprehensive medical, dental, and vision coverage; Flexible Spending Account (healthcare & dependent care); Health Savings Account (with high-deductible medical plan).

• Retirement: 401(k) plan with employer match.

• Time Off: Generous paid time off and paid holidays.

• Family Support: Paid parental leave plans for all new parents; paid family medical leave.

• Other Leaves: Paid military leave and disability leave.

• Investment & Development: Employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account.

• Support Services: Employee assistance program.

Working Hours: The role is advertised as requiring 40 hours per week, typical for a full-time position. Given the hybrid nature and focus on data analysis and tool development, some flexibility may be available outside of core working hours, although this is not explicitly stated.

📝 Enhancement Note: The salary range provided is competitive for a Senior UX Researcher role in a major tech hub like Raleigh, especially considering the specialized AI and data science components. The comprehensive benefits package is standard for a large enterprise like Red Hat and covers a wide array of employee needs. The mention of "office-flex" and "fully remote" in the company description, coupled with this role being "Hybrid," suggests that while not fully remote, there's an acknowledgment of flexible work arrangements.

🎯 Team & Company Context

🏢 Company Culture

Industry: Enterprise Open Source Software Solutions. Red Hat is a leader in providing open source software, including Linux, cloud, container, and Kubernetes technologies, utilizing a community-powered approach.

Company Size: Large enterprise, operating across 40+ countries with a significant number of associates. This scale implies robust processes, dedicated teams, and a structured approach to operations.

Founded: Red Hat was founded in 1993, bringing decades of experience and a deep-rooted culture in open source principles.

Team Structure:

• The UX Research Program and Practice team is a specialized unit focused on advancing research methodologies and tooling.

• The role likely involves collaboration with product managers, engineers, data scientists, and other UX professionals.

Methodology:

• Data-Driven Insights: Emphasis on using data to inform decisions, particularly through advanced analysis of participant data and research findings.

• AI Augmentation: Integration of AI and machine learning tools to enhance research efficiency, scale, and the depth of insights.

• Structured Knowledge Management: Focus on building queryable and machine-readable research repositories to enable broader access and utility of research findings.

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

📝 Enhancement Note: Red Hat's foundation in open source principles—transparency, collaboration, and community—heavily influences its culture. For a UX Researcher, this means an environment that values diverse perspectives, open discussion of ideas, and a collective approach to problem-solving. The company's large scale and global presence suggest a mature operational framework, which this role will contribute to by systematizing research data and tooling.

📈 Career & Growth Analysis

Operations Career Level: This is a Senior-level role, indicating a need for significant independent contribution, strategic thinking, and the ability to mentor or guide less experienced team members. The focus on developing new tools and methodologies suggests a path towards subject matter expertise in AI-augmented research operations.

Reporting Structure: The role reports into the UX Research Program and Practice team. This structure suggests a focus on professional development within the research discipline, with potential for collaboration with other specialized teams like Data Science or AI Engineering.

Operations Impact: This role's impact is multifaceted:

• Scalability: By developing AI tools and structured repositories, the researcher directly contributes to scaling research operations and making insights more accessible across the organization.

• Quality Improvement: Analyzing participant data and refining recruitment strategies enhances the quality and representativeness of research inputs.

• Strategic Insight: Conducting mixed-methods research and benchmarking provides deeper, more reliable insights for product strategy and development.

• Innovation: Contributing to AI-driven research tooling positions Red Hat at the forefront of research methodology.

Growth Opportunities:

• Specialization: Deepen expertise in AI/LLM applications within UX research, becoming a go-to person for AI-augmented research methodologies.

• Leadership: Potential to lead initiatives for developing and implementing research tooling, or to mentor junior researchers in data analysis and AI tool usage.

• Cross-functional Influence: Expand influence by integrating research data and insights into broader AI initiatives, product roadmaps, and engineering workflows.

• Industry Recognition: Contribute to Red Hat's reputation as an innovator in research practices through publications, conference presentations, or open-source contributions.

📝 Enhancement Note: The "Senior" title, combined with the AI and data-centric responsibilities, points to a role that is not just about executing research but about building the infrastructure and intelligence that powers it. Growth opportunities likely involve deepening technical expertise in AI and data science as applied to UX research, and potentially moving into technical leadership or strategic roles within the research function.

🌐 Work Environment

Office Type: Hybrid work environment. This implies a mix of remote work and in-office presence, likely balancing focused individual work with collaborative team sessions.

Office Location(s): Raleigh, North Carolina, United States. This location is a significant tech hub, offering access to talent and industry events.

Workspace Context:

• Collaborative Environment: Expect opportunities for in-person brainstorming, team meetings, and cross-functional collaboration with engineering, product, and data science teams.

• Technology Access: Access to advanced computing resources, software licenses (e.g., Qualtrics), and potentially internal development platforms for AI tooling.

• Focus & Innovation: The role requires dedicated time for deep analytical work and AI tool development, balanced with collaborative sessions for strategy and feedback.

Work Schedule: Standard 40-hour work week. The hybrid model suggests flexibility in structuring the workdays between remote and on-site, with core hours likely established for team synchronization. This structure supports continuous data analysis and iterative tool development.

📝 Enhancement Note: A hybrid environment in a tech hub like Raleigh suggests a professional setting that values both focused, individual contribution and dynamic team interaction. The emphasis on AI and data analysis within this role implies access to robust technological infrastructure and potentially specialized developer environments within the office.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Review of resume and portfolio to assess experience in mixed-methods research, AI tooling, and data analysis (SQL, R, Python).

• Technical Interview(s): Deep dive into technical skills, including SQL/R/Python querying, statistical concepts, AI/LLM tool evaluation, and prompt engineering. Expect coding challenges or data analysis exercises.

• Research Methodology & Strategy Interview: Discussion of past research projects, approach to complex problems, experience with benchmarking, and how you've integrated AI into research workflows. Portfolio presentation will be key here.

• Behavioral & Cultural Fit Interview: Assessment of collaboration style, adaptability, communication skills, and alignment with Red Hat's open source and inclusive culture.

• Hiring Manager Interview: Final conversation to discuss role expectations, team dynamics, and career growth.

Portfolio Review Tips:

• AI Tooling Showcase: Clearly articulate your contributions to AI-driven research tools, including challenges, methodologies, and measurable impacts on research efficiency or insight quality.

• Data Infrastructure Examples: Present projects where you've structured qualitative data, developed taxonomies, or contributed to research repositories. Demonstrate your ability to make data machine-readable.

• Code & Analysis Samples: Include well-commented code snippets (SQL, R, Python) demonstrating your analytical capabilities and proficiency in data manipulation and statistical analysis.

• Mixed-Methods Case Studies: Detail research projects that successfully integrated diverse data sources and methodologies, highlighting how you triangulated findings for stronger conclusions.

• Ethical Considerations: Be prepared to discuss your approach to research ethics, particularly regarding data privacy and bias mitigation in AI contexts.

Challenge Preparation:

• Data Analysis Challenge: You might be given a dataset and asked to perform specific analyses, identify trends, or draw conclusions, similar to what you would do with participant data.

• AI Tooling Scenario: You could be presented with a hypothetical research problem and asked how you would leverage or develop AI tools to address it, including evaluation criteria.

• Portfolio Presentation Practice: Rehearse presenting your portfolio, focusing on clear storytelling, quantifiable results, and articulating your specific contributions and thought processes.

📝 Enhancement Note: The interview process will likely be rigorous, testing both deep technical expertise in data science and AI, alongside core UX research competencies. A strong portfolio that directly showcases experience in AI tooling, data structuring, and advanced analytics will be crucial for advancing. Expect practical exercises that mirror the role's core responsibilities.

🛠 Tools & Technology Stack

Primary Tools:

• AI/LLM Platforms: Experience with AI and Large Language Models (LLMs) for research augmentation and tool development.

• Programming Languages: Proficiency in SQL for database querying, and R or Python for advanced data analysis, scripting, and statistical modeling.

• Survey Platforms: Expertise in Qualtrics for survey and screener design.

• Version Control: Familiarity with Git for code repositories and collaborative development.

Analytics & Reporting:

• Statistical Software: Proficiency with statistical analysis methods and potentially tools like R, Python libraries (Pandas, NumPy, SciPy), or specialized statistical packages.

• Data Visualization: Tools for presenting complex data and research findings effectively.

CRM & Automation:

• Data Management: Experience managing and analyzing data from various sources, potentially including CRM data if relevant to participant profiles or customer feedback.

• Workflow Automation: Understanding of how to integrate tools and automate parts of the research workflow, potentially using scripting or internal Red Hat platforms.

📝 Enhancement Note: The technology stack is heavily skewed towards data science and AI, with a clear requirement for programming skills (SQL, R/Python) and experience with AI/LLM tools. Qualtrics is specified for survey work. The mention of Git and collaboration with engineering teams suggests an environment where code is managed and developed iteratively, typical for software development organizations.

👥 Team Culture & Values

Operations Values:

• Open Source Principles: Adherence to transparency, collaboration, and community-driven innovation in all aspects of work.

• Data-Driven Decision Making: Valuing empirical evidence and rigorous analysis to inform research strategies and product decisions.

• Continuous Improvement: A commitment to iterating on processes, tools, and methodologies, especially in the rapidly evolving field of AI.

• Ethical Research Practices: Upholding high standards for participant privacy, data security, and unbiased research outcomes.

• Efficiency & Scalability: Driving initiatives that enhance the efficiency and scalability of research operations through automation and smart tooling.

Collaboration Style:

• Cross-Functional Integration: Working closely with engineering, product management, data science, and other research teams to share insights and align on strategies.

• Knowledge Sharing: Actively contributing to and leveraging shared knowledge bases, research repositories, and best practice discussions.

• Feedback-Driven Iteration: Embracing constructive feedback from peers and stakeholders to refine research designs, tools, and insights.

• Transparency in Process: Openly discussing research plans, methodologies, and findings to foster trust and alignment across teams.

📝 Enhancement Note: Red Hat's culture is deeply rooted in its open-source heritage. This translates to an environment where collaboration is key, transparency is expected, and the best ideas are valued regardless of tenure. For this role, it means actively engaging with diverse teams, sharing knowledge openly, and contributing to a collective effort to advance research practices through data and AI.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Ambiguity: The role operates at the cutting edge of AI in research, which can involve significant ambiguity in tool development and application. Adaptability and a proactive approach to defining scope are crucial.

• Balancing Rigor and Automation: Finding the optimal balance between leveraging AI for speed and scale, and maintaining the methodological rigor and validity of research findings.

• Data Complexity & Integration: Managing and structuring diverse data sources into a coherent, machine-readable repository presents significant technical and organizational challenges.

• Rapid Technological Evolution: Staying abreast of fast-paced advancements in AI, LLMs, and data science techniques to ensure tools and methodologies remain relevant and effective.

Learning & Development Opportunities:

• AI Specialization: Opportunity to become a leader in applying AI and LLMs to UX research, potentially attending specialized conferences or pursuing certifications in AI/ML.

• Data Engineering Skills: Enhance proficiency in data manipulation, database management, and building data pipelines that support research operations.

• Methodological Innovation: Contribute to developing novel research frameworks and AI-powered tools that could set new industry standards.

• Cross-Disciplinary Exposure: Gain deeper insights into software development lifecycles, data science methodologies, and enterprise-level product strategy.

📝 Enhancement Note: The primary challenges stem from the innovative and evolving nature of AI in research. Successfully navigating these will require a blend of technical acumen, strategic foresight, and strong problem-solving skills. The growth opportunities are significant for those looking to specialize at the intersection of UX research, data science, and AI.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you developed or significantly improved an AI-driven research tool. What was the problem, your approach, and the impact?" (Focus on your AI tooling development and evaluation experience.)

• "How would you design a system to make qualitative research findings machine-readable and queryable for downstream AI applications? What are the key considerations for schema and taxonomy?" (Test your understanding of research repository concepts.)

• "Walk us through a complex data analysis project you led using SQL, R, or Python. What was the objective, your methodology, and the key insights derived?" (Demonstrate your data analysis and programming skills.)

Company & Culture Questions:

• "How do Red Hat's open source principles of transparency, collaboration, and community influence your approach to research and team interaction?" (Gauge cultural alignment.)

• "Red Hat is a large enterprise. How would you approach integrating new AI research tools or data structures within such an environment, considering existing processes and stakeholder needs?" (Assess your change management and stakeholder influence skills.)

Portfolio Presentation Strategy:

• Structure Your Narrative: For each project, clearly define the problem, your role and approach, the tools and methods used (especially AI and data analysis), the results (quantified where possible), and your key learnings.

• Highlight AI & Data Impact: Explicitly call out your contributions related to AI tooling, data structuring, and advanced analytics. Use visuals or demos if possible for tools.

• Showcase Technical Proficiency: Be ready to briefly explain code snippets or analytical workflows, demonstrating your technical command.

• Address Ethical Considerations: Seamlessly weave in how you handled research ethics, data privacy, and bias mitigation in your projects.

• Tailor to Red Hat: Connect your experience to Red Hat's mission, products, and open-source culture where relevant.

📝 Enhancement Note: Preparation should focus on articulating specific, quantifiable achievements related to AI tooling, data infrastructure, and advanced analytics. Being able to discuss the "how" and "why" behind your technical and methodological choices will be critical, as will demonstrating an understanding of and alignment with Red Hat's unique culture.

📌 Application Steps

To apply for this Senior UX Researcher AI position:

• Submit Your Application: Utilize the provided application link to submit your resume and any requested supporting documents.

• Portfolio Customization: Tailor your resume and portfolio to prominently feature your experience with AI-driven research tools, advanced data analysis (SQL, R, Python), research repository contributions, and mixed-methods research. Prepare specific examples and case studies that highlight your operationalizing of research data and tooling.

• Resume Optimization: Ensure your resume clearly outlines your 5+ years of relevant experience, technical skills (SQL, R, Python, Qualtrics, AI/LLM tools), and achievements in enterprise product development environments. Use keywords from the job description naturally.

• Interview Preparation: Practice presenting your portfolio, focusing on your contributions to AI tooling, data structuring, and analytical projects. Prepare to discuss your approach to research ethics, AI reliability, and bias mitigation. Rehearse answers to strategy and behavioral questions.

• Company Research: Thoroughly research Red Hat's products, open-source culture, and recent initiatives. Understand how your role as a Senior UX Researcher AI contributes to their broader technology strategy and commitment to innovation.

⚠️ 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.