Staff UX Researcher - AI Agents

📍 Job Overview

Job Title: Staff UX Researcher - AI Agents

Company: Okta

Location: Bellevue, Washington; Chicago, Illinois; New York, New York; San Francisco, California; Seattle, Washington; Toronto, Ontario, Canada; Washington, DC

Job Type: Full-Time

Category: User Experience Research / Product Operations

Date Posted: August 14, 2026

Experience Level: 7+ Years

Remote Status: Hybrid

🚀 Role Summary

• Lead foundational and evaluative UX research for AI agents and automated workflows within Okta's identity and security platform, focusing on non-deterministic AI experiences.

• Drive mixed-methods research strategies, integrating qualitative depth (e.g., contextual inquiry, cognitive walkthroughs) and quantitative rigor (e.g., surveys, behavioral telemetry, statistical modeling) to assess AI product effectiveness and user experience.

• Pioneer AI-driven research operations by actively utilizing AI tools and prompt engineering to scale qualitative coding, data synthesis, and research output delivery across the organization.

• Facilitate strategic collaboration and alignment among cross-functional teams, including Product, Design, ML Engineering, and Data Science, to drive product impact and executive decision-making.

📝 Enhancement Note: This role is positioned at a "Staff" level, indicating a high degree of autonomy, strategic influence, and expectation for mentorship. The focus on "AI Agents" and "Identity, Security, and Artificial Intelligence" places this role at the cutting edge of product development, requiring a deep understanding of complex B2B environments and the unique challenges of researching non-deterministic user experiences. The emphasis on "AI-Forward Research" and "AI Research Operations" suggests a need for candidates who can not only conduct research but also innovate the research process itself using AI.

📈 Primary Responsibilities

• Construct and own an AI-forward research roadmap for key product pillars, operating at the intersection of Identity, Security, and Artificial Intelligence.

• Lead foundational and evaluative research on autonomous AI agents and their security implications, defining user experiences for these novel interactions.

• Execute balanced mixed-methods research, driving qualitative depth and quantitative rigor to evaluate AI products and user behaviors.

• Integrate AI tools, prompt engineering, and agentic research workflows into daily practice to accelerate research outputs and scale insights.

• Facilitate research to drive cross-discipline clarity, executive alignment, and decision-making across complex security and administrator workflows.

• Design mechanisms to cultivate deep customer empathy and knowledge of partners, end-users, consumers, administrators, and other identified audiences.

• Represent research findings to diverse audiences across the organization through compelling storytelling that motivates action on complex user needs.

• Mentor and coach team members to raise the methodological and technical bar for UX research within the organization.

📝 Enhancement Note: The responsibilities highlight a blend of strategic research leadership and hands-on execution. The expectation to "own" the roadmap and "drive" research implies significant autonomy and strategic input. The specific mention of "non-deterministic AI agents" and "security thereof" points to the critical nature of the work and the need for robust, nuanced research methodologies. The emphasis on "AI Research Operations" and using AI tools for synthesis and scaling is a key differentiator for this role.

🎓 Skills & Qualifications

Education: Master’s or Ph.D. in Human-Computer Interaction (HCI), Cognitive Psychology, Computer Science, Data Science, Social Sciences, or equivalent industry experience.

Experience: 7+ years of hands-on UX research experience in complex B2B, developer, or enterprise software environments, with a track record of driving product impact at a Senior or Staff level.

Required Skills:

• Demonstrated expertise in leading foundational and evaluative research for complex software products.

• Equal mastery across both Qualitative research methodologies (e.g., contextual inquiry, cognitive walkthroughs, in-depth interviews) and Quantitative research methodologies (e.g., large-scale surveys, behavioral telemetry analysis, statistical modeling).

• Active daily practitioner of AI tools and prompt engineering workflows to streamline research processes, including synthesis, structuring unstructured data, and scaling insight delivery.

• Proven experience with products made for developers, administrators, or other technical audiences; experience in security products is a strong advantage.

• Strong strategic problem-solving abilities, with the capacity to plan, prioritize, and organize complex research initiatives independently.

• History of effective communication, compelling storytelling, and building alignment across all levels of leadership and cross-functional partners (Product, Design, ML Engineering, Data Science).

• Ability to mentor and coach junior researchers, contributing to team growth and raising methodological standards. Preferred Skills:

• SQL, R, or Python for data analysis and statistical modeling.

• Experience with advanced quantitative techniques such as Cluster Analysis and Advanced Statistical Modeling.

• Proficiency in designing and analyzing A/B tests for product optimization.

• Familiarity with Human-in-the-Loop (HITL) UX evaluation methodologies.

• Experience with advanced prompt engineering for AI operations and agentic workflow evaluation.

• Knowledge of synthetic data validation techniques.

📝 Enhancement Note: The requirement for "equal mastery" across qualitative and quantitative methods at a Staff level is significant, indicating a need for deep, practical experience in both, not just theoretical knowledge. The explicit mention of "active daily practitioner of AI tools" is a critical qualifier, suggesting that candidates must demonstrate current, hands-on experience leveraging AI for research tasks. The preferred skills indicate a strong leaning towards data science and advanced analytics, crucial for evaluating AI-driven products.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase a minimum of 2-3 complex B2B or enterprise software projects where you led the UX research strategy and execution from inception to impact.

• Clearly demonstrate your ability to apply both qualitative and quantitative methodologies to solve complex user problems and inform product decisions.

• Include examples where you leveraged AI tools or novel approaches to enhance research efficiency, scale insights, or tackle unique research challenges.

• Present case studies that highlight your ability to drive product impact, influence strategy, and achieve measurable outcomes through research insights.

• For each project, articulate the problem statement, your research approach, key findings, and the resulting product or business impact. Process Documentation:

• Provide examples of how you have documented research processes, from planning and recruitment to analysis and insight dissemination.

• Showcase instances where you have developed or improved research workflows, particularly those that enhanced efficiency or scalability.

• Demonstrate your ability to synthesize large volumes of qualitative and quantitative data into actionable insights and clear, concise reports or presentations.

• Include examples of how you have collaborated with engineering and data science teams to integrate research findings into product development pipelines and operational metrics.

📝 Enhancement Note: For a Staff-level role, especially one focused on AI and research operations, the portfolio needs to go beyond individual studies. It should demonstrate strategic thinking, process ownership, and the ability to scale research impact. Evidence of using AI to improve research operations will be a significant advantage. The emphasis should be on the impact of the research, not just the methods used.

💵 Compensation & Benefits

Salary Range:

• San Francisco Bay Area, California: $174,000 - $240,000 USD annually.

• California (excluding Bay Area), Colorado, Illinois, New York, Washington: $156,000 - $215,000 USD annually.

• Canada: $140,000 - $192,000 CAD annually.

Benefits:

• Comprehensive health, dental, and vision insurance.

• 401(k) with company match (US) / RRSP with a match (Canada).

• Flexible Spending Account (US).

• Healthcare Spending Account (Canada).

• Telemedicine services (Canada).

• Generous paid leave, including Paid Time Off (PTO) and parental leave.

• Equity (where applicable).

• Bonus opportunities.

Working Hours: Standard 40 hours per week, with flexibility expected for research project demands and cross-functional collaboration across time zones.

📝 Enhancement Note: The salary ranges provided are for base salary only. The company explicitly states that total compensation includes equity, bonus, and benefits. The ranges are differentiated by location, reflecting regional market differences in the US and Canada. The methodology for these ranges is based on the provided data, with the lower end representing entry to the defined experience level and the higher end representing a senior Staff researcher with extensive impact. The inclusion of specific benefits like telemedicine and healthcare spending accounts adds value for candidates.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology - Identity and Access Management (IAM), Cloud Security, AI. Okta operates in a critical sector of the technology landscape, providing foundational security infrastructure that enables digital transformation and the secure adoption of new technologies like AI. This context means the work is high-stakes, requiring meticulous attention to detail and a strong understanding of security principles.

Company Size: Okta is a large enterprise, indicated by its status as a publicly traded company and the breadth of its product offerings and global presence. This size implies structured processes, established career paths, and significant cross-functional collaboration opportunities.

Founded: Okta was founded in 2009. Its relatively recent founding, coupled with its rapid growth and market leadership, suggests a dynamic, innovative, and fast-paced culture that values agility and forward-thinking solutions, especially in emerging areas like AI.

Team Structure:

• The UX Research team at Okta likely comprises researchers with diverse specializations, supporting various product lines and platforms. Given the Staff-level role, this position may involve mentoring or leading smaller research initiatives or contributing to the strategic direction of the entire UX research function.

• Reporting is expected to be within a Product Design or Product Management hierarchy, with strong dotted-line reporting to Product and Engineering leads for specific projects.

• Cross-functional collaboration is paramount, involving close partnerships with Product Managers, Designers, ML Engineers, Data Scientists, and Security Architects to ensure research insights are integrated into product development and AI strategy. Methodology:

• Data-driven decision-making is a core operational principle at Okta, with a strong emphasis on leveraging both qualitative and quantitative data to inform product strategy and user experience.

• Workflow planning and optimization are critical, especially in the context of scaling research operations and integrating AI tools to enhance efficiency and impact.

• Automation and efficiency practices are actively being explored and implemented, particularly with the integration of AI tools for research tasks, aiming to accelerate insight delivery and broaden research reach.

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

📝 Enhancement Note: Okta's focus on "Identity is the key to unlocking the potential of AI" and securing AI by building "trusted, neutral infrastructure" underscores the company's strategic positioning. This implies a culture that values innovation, trust, and security. The mention of "builders and owners who operate with speed and urgency and execute with excellence" points to a high-performance culture. The emphasis on "career-defining work" suggests a challenging but rewarding environment.

📈 Career & Growth Analysis

Operations Career Level: This role is a "Staff" level UX Researcher. In operations and product development contexts, Staff roles are typically senior individual contributors who are expected to operate with significant autonomy, influence product strategy, tackle the most complex problems, and mentor others. They are key technical leaders within their domain, often shaping best practices and driving innovation.

Reporting Structure: The Staff UX Researcher will likely report to a Director or VP of Product Design or UX Research. They will work closely with Product Managers, Engineering Leads (particularly ML Engineers), and Design Leads on specific product initiatives, influencing roadmaps and strategic decisions.

Operations Impact: The impact of this role is significant. By researching and defining user experiences for AI agents and automated workflows in identity and security, this researcher will directly influence how organizations leverage AI securely. Their insights will shape product strategy, drive adoption of new features, ensure user trust, and ultimately contribute to Okta's leadership in the AI security space. The use of AI to scale research operations also has a direct operational efficiency impact.

Growth Opportunities:

• Specialization: Deepen expertise in AI/ML user experience research, identity security, or advanced quantitative methodologies.

• Leadership: Transition into a management role, leading a team of UX researchers, or take on strategic leadership roles within the broader product organization, shaping research practice and operational efficiency.

• Cross-Functional Expertise: Develop deeper understanding and influence in adjacent areas like Product Management, Machine Learning Engineering, or Data Science, potentially leading to hybrid roles or strategic advisory positions.

• Methodological Innovation: Lead initiatives to define and implement new research methods for AI-driven products or advance the use of AI in research operations.

📝 Enhancement Note: The Staff level signifies a transition from individual contributor to a role with significant influence and potential for mentorship. The growth opportunities are geared towards deepening technical expertise, moving into leadership, or broadening strategic impact across product and operations. The AI focus presents a unique opportunity for career advancement at the intersection of UX, AI, and security.

🌐 Work Environment

Office Type: Hybrid work environment. This means a combination of remote work and in-office collaboration. The role requires being present in one of the specified office locations for a portion of the week to facilitate in-person collaboration, team building, and access to on-site resources.

Office Location(s): Bellevue, Washington; Chicago, Illinois; New York, New York; San Francisco, California; Seattle, Washington; Toronto, Ontario, Canada; Washington, DC. Candidates are expected to be based in or willing to relocate to one of these designated hubs.

Workspace Context:

• The workspace is designed to foster collaboration, innovation, and community. Expect a mix of individual focused work areas and collaborative spaces for team meetings, brainstorming sessions, and design thinking workshops.

• Access to modern UX research tools, prototyping software, and potentially advanced analytics and AI-powered research platforms will be available.

• Regular opportunities for interaction with product, design, engineering, and data science teams, both formally in meetings and informally in shared spaces, to foster a cohesive and integrated product development culture.

Work Schedule: While the standard work week is 40 hours, the nature of research, especially with AI and complex B2B products, may require flexibility. This could involve occasional extended hours during critical research phases, data analysis sprints, or when collaborating with global teams across different time zones. The hybrid model aims to balance work-life integration with the needs of collaborative product development.

📝 Enhancement Note: The hybrid nature of the work environment is a key aspect. The multiple location options offer flexibility for candidates, but the requirement to be in-office suggests an emphasis on in-person collaboration, which is often crucial for complex problem-solving and team cohesion in roles like this. The "immersive, in-person onboarding experience" mentioned in the company description reinforces this.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review your application and resume, focusing on relevant experience, particularly with Staff-level UX research, B2B/enterprise software, and AI/ML.

• Hiring Manager Interview: A discussion with the hiring manager to assess your experience, strategic thinking, leadership potential, and alignment with the team's goals and culture. You'll likely discuss your approach to research roadmapping and managing complex projects.

• Portfolio Review: A dedicated session where you will present 1-2 key projects from your portfolio. Focus on demonstrating your strategic impact, mixed-methods expertise, and experience with complex B2B/enterprise environments. Be prepared to discuss your process, decision-making, and the outcomes of your research, especially any involving AI or novel approaches.

• Cross-Functional Interviews: Interviews with peers from Product Management, Design, and ML Engineering/Data Science. These sessions will assess your collaboration skills, ability to communicate complex research findings effectively, and your understanding of technical constraints and product development lifecycles.

• Staff-Level Challenge/Case Study: You may be given a take-home assignment or an in-person case study focused on a hypothetical AI agent research problem. This will test your ability to apply your mixed-methods skills, strategic thinking, and prompt engineering knowledge to a realistic scenario.

• Final Interview: Often with a senior leader (e.g., VP of Design/Research) to assess overall fit, strategic vision, and potential for long-term impact within Okta.

Portfolio Review Tips:

• Curate Strategically: Select projects that best showcase your Staff-level capabilities: strategic impact, complex problem-solving, mixed-methods mastery, and ideally, experience with AI or technical products.

• Tell a Story: For each project, frame it as a narrative: the business/user problem, your research strategy and methods (qualitative and quantitative), key insights, the impact of those insights on the product or business, and lessons learned.

• Quantify Impact: Whenever possible, use metrics to demonstrate the impact of your research (e.g., "Our research led to a X% increase in user adoption," or "Identified usability issues that were addressed, reducing support tickets by Y%").

• Highlight AI/ML Experience: If you have experience with AI or ML products, or have used AI tools in your research process, make this a focal point. Explain how you used AI and what the benefits were.

• Be Prepared for Questions: Anticipate questions about your decision-making process, how you handle conflicting data, how you influence stakeholders, and how you mentor others.

Challenge Preparation:

• Understand the Domain: Familiarize yourself with Okta's products, the identity and access management space, and current trends in AI agents and their applications.

• Practice Mixed-Methods: Be ready to outline how you would approach a research problem using a combination of qualitative and quantitative methods.

• AI Tool Proficiency: Prepare to discuss your experience with prompt engineering and how you would use AI tools to accelerate research tasks like synthesis, coding, or generating hypotheses.

• Structure Your Response: For case studies, clearly outline your approach, including research questions, proposed methods, potential challenges, and how you would measure success.

📝 Enhancement Note: The interview process is designed to rigorously assess not just research skills but also strategic thinking, collaboration, and leadership potential, aligning with the Staff-level expectations. The portfolio review is a critical component, requiring candidates to demonstrate tangible impact and specialized experience, particularly with AI and complex software.

🛠 Tools & Technology Stack

Primary Tools:

• UX Research Platforms: Tools for survey creation, user testing (e.g., UserTesting.com, Lookback), and participant recruitment.

• Data Analysis Software: Statistical packages (e.g., SPSS, R, Python libraries like SciPy, NumPy, Pandas) for quantitative analysis.

• Qualitative Analysis Tools: Software for thematic analysis, affinity mapping, and synthesis (e.g., Dovetail, NVivo, Miro,

FigJam).

• AI & Prompt Engineering Tools: Active daily use of AI assistants and prompt engineering techniques for tasks such as:

  • Qualitative data coding and thematic synthesis.

  • Structuring unstructured data (e.g., interview transcripts, survey open-ends).

  • Scaling insight delivery and generating research summaries.

  • Assisting in survey design and question generation.

  • Potentially exploring agentic workflows for research automation. Analytics & Reporting:

• Behavioral Telemetry: Experience with tools that track user interactions within software (e.g., Pendo, Mixpanel, Amplitude) for quantitative behavioral analysis.

• SQL: Proficiency in SQL for querying databases and extracting behavioral data.

• Data Visualization Tools: Tools like Tableau, Power BI, or Looker for creating dashboards and communicating quantitative findings.

CRM & Automation:

• While not directly a CRM role, understanding how user data is managed within CRM systems and how research insights can inform CRM strategies is beneficial.

• Familiarity with automation concepts in research operations, potentially involving scripting or using AI for workflow optimization.

• Integration tools awareness might be relevant if research data needs to be fed into other systems.

📝 Enhancement Note: The explicit mention of "Active daily practitioner of AI tools and prompt workflows" is paramount. Candidates should be prepared to discuss specific AI tools they use, their prompt engineering strategies, and how these tools have tangibly improved their research process and outputs. The inclusion of SQL, R/Python, and behavioral telemetry tools indicates a strong quantitative component to the role.

👥 Team Culture & Values

Operations Values:

• Customer Empathy: A deep commitment to understanding and advocating for the user, ensuring their needs are at the forefront of product development, especially for complex AI interactions.

• Data-Driven Rigor: A belief in the power of both qualitative and quantitative data to inform decisions, demanding robust methodologies and thorough analysis.

• Innovation & Speed: A culture that embraces new technologies (like AI) and operates with urgency to solve complex problems and deliver impactful solutions quickly.

• Collaboration & Transparency: Open communication and close partnership across product, design, engineering, and data science teams to ensure alignment and shared understanding.

• Continuous Learning: A dedication to staying at the forefront of UX research methodologies, AI advancements, and the evolving landscape of identity security.

Collaboration Style:

• Cross-Functional Integration: Researchers are embedded within product teams, working closely with Product Managers, Designers, and Engineers from the outset of research initiatives.

• Process Improvement Focus: A culture that actively seeks opportunities to optimize research workflows, leverage new tools (especially AI), and increase the efficiency and impact of the research function.

• Knowledge Sharing: Encouraging the sharing of insights, best practices, and lessons learned across the research team and broader product organization through presentations, documentation, and informal discussions.

📝 Enhancement Note: Okta's stated values of "speed and urgency," "execute with excellence," and "builders and owners" suggest a high-performance culture. For this role, it means demonstrating initiative, taking ownership of research initiatives, and consistently delivering high-quality, impactful work. The emphasis on AI and identity security implies a culture that values cutting-edge technology and robust security practices.

⚡ Challenges & Growth Opportunities

Challenges:

• Researching Non-Deterministic AI: The primary challenge will be developing effective methodologies to understand and design user experiences for AI agents whose behavior can be unpredictable or context-dependent, ensuring safety and usability.

• Balancing Qualitative Depth and Quantitative Breadth: Effectively integrating deep qualitative understanding with broad quantitative validation at scale, especially within a fast-paced AI development cycle.

• Integrating AI into Research Practice: Successfully adopting and scaling AI tools for research operations requires continuous learning, prompt engineering expertise, and careful validation to maintain research integrity.

• Cross-Discipline Alignment: Navigating complex stakeholder landscapes, including ML Engineering and Data Science, to ensure research findings are understood, integrated, and acted upon, particularly concerning AI model behavior and user impact.

• Defining "Good" UX for AI: Establishing clear criteria and metrics for what constitutes a successful user experience for AI agents, which differs significantly from traditional software.

Learning & Development Opportunities:

• AI/ML UX Specialization: Opportunity to become a leading expert in the UX of AI agents and machine learning-driven products.

• Advanced Methodologies: Deepen expertise in advanced quantitative techniques (statistical modeling, causal inference) and cutting-edge qualitative methods for AI evaluation.

• Research Operations Leadership: Develop skills in scaling research functions, implementing new technologies (AI), and optimizing research workflows for maximum efficiency and impact.

• Industry Influence: Contribute to the broader UX and AI research community through presentations, publications, or internal best practice development.

• Mentorship: Grow leadership skills by mentoring junior researchers and influencing the strategic direction of UX research at Okta.

📝 Enhancement Note: The challenges are directly tied to the cutting-edge nature of the role, particularly in researching AI agents. The growth opportunities are substantial, offering pathways to become a thought leader in a rapidly evolving field.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to research a product or feature that had no clear precedent. How did you approach it, and what was the outcome?" (Assesses ability to tackle novel problems, like AI agents.)

• "Walk me through a complex B2B/enterprise product you significantly influenced through your UX research. What was the problem, your approach, and the measurable impact?" (Tests strategic impact and stakeholder influence.)

• "How have you used AI tools in your research process to date? Provide specific examples of prompts, tools, and the resulting efficiency or insight improvements." (Crucial for this role; assess practical AI application.)

• "Describe a situation where you had to integrate qualitative and quantitative findings that seemed to conflict. How did you resolve it and present your conclusions?" (Assesses mixed-methods mastery and analytical rigor.)

• "How would you approach building a research roadmap for a new AI agent feature with ambiguous user needs and high security implications?" (Tests strategic planning and understanding of AI/security context.) Company & Culture Questions:

• "What excites you about Okta's mission to secure AI?" (Assesses alignment with company vision.)

• "How do you approach building empathy for technical users like administrators or developers?" (Tests understanding of target audience and user-centricity.)

• "Describe your experience mentoring or coaching junior researchers. What is your philosophy on developing talent?" (Assesses leadership and team development potential.)

• "How do you ensure your research findings lead to tangible product improvements and business impact in a fast-paced environment?" (Tests actionability and results orientation.) Portfolio Presentation Strategy:

• Focus on Impact: For each project, quantify the impact of your research. Use metrics to show how your insights drove product decisions, improved user experience, or influenced business outcomes.

• Showcase Methodological Breadth: Clearly articulate why you chose specific qualitative and quantitative methods for each project and how they complemented each other.

• Highlight AI Integration: If any projects involved AI or you used AI tools in your research process, dedicate time to explaining this clearly. Detail your prompt engineering approach and the benefits derived.

• Structure for Clarity: Use a consistent structure for each case study: Problem -> Your Role/Approach -> Methods (Qual/Quant) -> Key Insights -> Impact/Outcome -> Lessons Learned.

• Be Ready for Deep Dives: Expect interviewers to ask probing questions about your methodology, decision-making, and the challenges you faced. Be prepared to discuss alternatives you considered.

📝 Enhancement Note: Interview preparation should focus on demonstrating leadership, strategic thinking, practical AI tool usage, and a deep understanding of mixed-methods research in complex B2B environments. The portfolio is the primary evidence, so practicing its presentation is key.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Okta careers portal via the provided URL.

• Tailor your resume: Highlight your Staff-level UX research experience, specific achievements in B2B/enterprise software, and any experience with AI/ML products or research operations. Quantify your impact wherever possible.

• Prepare your portfolio: Curate 2-3 of your most impactful projects, focusing on those demonstrating strategic influence, mixed-methods expertise, and ideally, experience with AI or complex technical products. Ensure clear documentation of your process, insights, and outcomes.

• Practice your presentation: Rehearse presenting your portfolio projects, focusing on storytelling, clarity, and demonstrating your ability to articulate complex research strategies and their impact. Be ready to discuss your AI tool usage and prompt engineering skills.

• Research Okta: Understand Okta's mission, products (especially in identity and security), and their strategic focus on AI. Prepare to discuss why you are specifically interested in this role and Okta.

⚠️ 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.