Senior UX Researcher, Applied AI Solution

📍 Job Overview

Job Title: Senior UX Researcher, Applied AI Solution

Company: Amazon

Location: Sunnyvale, California, United States; Seattle, Washington, United States; New York, New York, United States

Job Type: Full-Time

Category: User Experience Research / Applied AI

Date Posted: 2026-08-20

Experience Level: 5-10 Years

Remote Status: On-site

🚀 Role Summary

• Lead high-impact, end-to-end UX research programs for AI-native business applications within AWS, focusing on understanding customer needs and informing product strategy.

• Partner closely with cross-functional teams including Design, Product Management, Engineering, and Applied Science to translate complex research findings into actionable recommendations.

• Drive research initiatives in ambiguous and complex problem spaces, utilizing strong methodological judgment to de-risk product and design decisions and increase adoption confidence.

• Elevate research craft and decision-making quality across the team through mentorship, thought partnership, and adherence to high research standards.

📝 Enhancement Note: This role is positioned as a Senior UX Researcher within Amazon's AWS Applied AI Solutions team. The emphasis on "Applied AI Solution" and "AI-native business applications" suggests a focus on practical, customer-facing AI products rather than purely theoretical research. The "Senior" title and responsibilities imply a need for independent leadership, strategic thinking, and the ability to mentor junior researchers. The core objective is to ensure customer evidence drives both immediate product improvements and long-term strategic direction for AI solutions.

📈 Primary Responsibilities

• Lead comprehensive UX research programs from inception to delivery, encompassing study design, participant recruitment, moderation, rigorous analysis, insightful synthesis, and clear communication of findings to stakeholders.

• Execute a diverse range of research methodologies, including but not limited to, rapid evaluative studies, foundational qualitative research (interviews, ethnography, diary studies, journey mapping), quantitative surveys, and complex mixed-methods programs.

• Collaborate strategically with senior-level cross-functional partners to define product roadmaps, evaluate novel concepts and prototypes, and iterate on existing customer experiences to drive adoption and satisfaction.

• Independently define research plans for ill-defined problem spaces, applying sound methodological judgment to select appropriate research approaches that align with decision-making needs, product development stages, and risk mitigation objectives.

• Proactively identify unmet customer needs, product gaps, adoption barriers, and trust concerns within AI-driven business solutions to minimize ambiguity and de-risk significant investments.

• Synthesize complex research data into clear, concise, and actionable recommendations that directly enhance usability, customer adoption, trust, and overall experience quality for AI-powered applications.

• Influence product roadmaps and prioritization decisions by ensuring customer evidence is consistently visible, credible, and actionable at critical junctures of the product development lifecycle.

• Develop deep domain expertise in customer workflows, emerging AI technologies, competitive landscapes, and industry trends to provide informed strategic guidance.

• Mentor and guide junior UX researchers, fostering their professional development and contributing to the overall enhancement of research quality, consistency, and impact across the team.

• Explore and integrate innovative applications of AI within the research and product development process to improve efficiency, depth of insight, and overall quality of outcomes.

📝 Enhancement Note: The responsibilities highlight a blend of tactical execution and strategic influence. The emphasis on "ambiguous problem spaces," "methodological judgment," and "influencing roadmaps" points to a role that requires not just research execution but also strategic foresight and the ability to shape product direction. The mentorship aspect is crucial for a senior role, indicating expectations for leadership and team development.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Human-Computer Interaction (HCDE), Human Factors, Cognitive Psychology, or a closely related field.

• Master's degree in Human Factors, HCDE, Cognitive Psychology, or a related field is preferred. Experience:

• A minimum of 5 years of demonstrated success in leading User Research projects with a clear track record of impact on product development and customer outcomes.

• Extensive hands-on experience across a broad spectrum of research methodologies, including but not limited to: field research, ethnography, lab-based user testing, remote usability testing, paper prototype testing, iterative prototype testing, concept validation, and survey design and execution.

• Proven experience managing all phases of the research lifecycle: meticulous study design, effective participant recruitment, skilled moderation, robust analysis, comprehensive reporting, and impactful synthesis.

• Solid experience with behavioral data collection techniques, quantitative data analysis, and fundamental statistical principles. Required Skills:

• User Research Leadership: Proven ability to lead end-to-end research programs, from strategic planning to final delivery of actionable insights.

• Methodological Expertise: Deep understanding and practical application of diverse qualitative and quantitative research methods relevant to complex software products.

• Cross-Functional Collaboration: Demonstrated ability to partner effectively with Design, Product Management, Engineering, and Applied Science teams to integrate research findings into product strategy and development.

• Data Synthesis & Communication: Skill in translating complex research data into clear, compelling, and actionable recommendations for diverse audiences, including senior leadership.

• Problem Framing: Ability to define research questions and plans for ambiguous problem spaces, demonstrating strong judgment in selecting appropriate methodologies.

Preferred Skills:

• Technical Domain Experience: Prior experience in a technical field such as software development, network development, IT, or a related area, providing context for AI-native business applications.

• International Research: Experience conducting UX research in non-US markets, understanding cultural nuances and diverse user behaviors.

• Agile/Scrum Proficiency: Experience working within highly Agile or Scrum development environments, adapting research practices to fast-paced iterative cycles.

• AI/ML Understanding: Familiarity with AI and Machine Learning concepts as they apply to user experience and product development.

📝 Enhancement Note: The requirement for a portfolio is explicitly stated, emphasizing the need for candidates to showcase tangible evidence of their research impact. The inclusion of "Applied AI Solution" and "AWS" suggests that candidates with experience in enterprise software, cloud services, or AI/ML-related UX will be highly competitive. The distinction between "Basic" and "Preferred" qualifications guides candidates on where to focus their application materials.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• A comprehensive portfolio is mandatory, showcasing a range of past work experience and key deliverables. This should include examples of study plans, detailed research reports, developed personas, journey maps, and other relevant artifacts that demonstrate research impact.

• Evidence of leading end-to-end research programs, highlighting the candidate's ability to manage projects from initial scoping through to final insight delivery and influence.

• Examples demonstrating the application of diverse research methodologies, illustrating adaptability and methodological rigor in various research contexts.

• Case studies that clearly articulate the problem, research approach, key findings, and the tangible impact of the research on product decisions, user experience, or business outcomes. Process Documentation:

• Showcase the ability to document research processes clearly, including detailed study designs that outline objectives, hypotheses, methodologies, participant criteria, and analysis plans.

• Provide examples of how research findings are synthesized and communicated, demonstrating the creation of actionable insights and strategic recommendations that can directly inform product roadmaps and design iterations.

• Illustrate experience with iterative research cycles, showing how feedback from early findings has been incorporated into subsequent research phases or product development adjustments.

• Demonstrate an understanding of how to connect research outcomes to measurable business objectives, such as adoption rates, customer satisfaction, or efficiency gains for AI-powered solutions.

📝 Enhancement Note: For a Senior UX Researcher role, particularly at a company like Amazon, the portfolio is critical. It's not just about listing projects but demonstrating the impact of that research. Candidates should prepare to walk through their portfolio, explaining their thought process, the challenges faced, and the quantifiable or qualitative outcomes achieved. The focus on "AI-native business applications" means highlighting any relevant experience with enterprise software or complex B2B solutions.

💵 Compensation & Benefits

Salary Range:

• Sunnyvale, California: $167,400 - $226,500 USD annually

• New York, New York: $167,400 - $226,500 USD annually

• Seattle, Washington: $152,200 - $205,900 USD annually

Benefits:

• Comprehensive health insurance package including medical, dental, and vision coverage.

• Prescription drug insurance and access to Basic Life & AD&D insurance, with options for Supplemental life plans.

• Employee Assistance Program (EAP) and robust Mental Health Support services.

• Access to a Medical Advice Line for health-related inquiries.

• Flexible Spending Accounts (FSAs) for healthcare and dependent care expenses.

• Support for family growth through Adoption and Surrogacy Reimbursement coverage.

• Competitive retirement savings plan with 401(k) matching.

• Generous Paid Time Off (PTO) policy.

• Comprehensive Parental Leave benefits.

• Equity compensation in the form of Restricted Stock Units (RSUs).

• Potential for Sign-on payments to supplement initial compensation. Working Hours:

• Standard full-time commitment, typically around 40 hours per week, with flexibility often available depending on project needs and team dynamics.

📝 Enhancement Note: The salary ranges provided are specific to each listed location, reflecting regional cost of living and market demand. The inclusion of "sign-on payments and restricted stock units (RSUs)" as part of the total compensation package is typical for senior roles at Amazon, indicating that the base salary is only one component of the overall remuneration. Benefits are extensive, covering health, wellness, financial planning, and family support.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Cloud Computing, Artificial Intelligence, Software Development)

Company Size: Amazon is a global technology giant with hundreds of thousands of employees worldwide, operating across numerous sectors including e-commerce, cloud computing, digital streaming, and artificial intelligence.

Founded: 1994, by Jeff Bezos. Amazon has a long history of innovation, customer obsession, and pioneering new markets, including cloud services with AWS.

Team Structure:

• The AWS Applied AI Solutions team is a specialized unit within Amazon Web Services, focusing on developing and deploying AI-powered business applications.

• Researchers on this team typically report into a research lead or a director within the broader AWS UX or Product organization, with close dotted-line reporting to Product and Design leads within their specific product area.

• This role requires significant cross-functional collaboration with Product Managers, Engineers, Applied Scientists, UX Designers, and Marketing teams, necessitating strong communication and partnership skills. Methodology:

• Data-Driven Decision Making: Amazon's culture heavily emphasizes data and metrics to inform decisions. Research insights are expected to be backed by robust data and presented with clear metrics.

• Customer Obsession: A core tenet of Amazon's culture is deep customer focus. Research is paramount in understanding and advocating for customer needs.

• Bias for Action & Iteration: Teams are encouraged to move quickly, test hypotheses, and iterate based on feedback. Research often needs to balance rigor with speed to support agile development cycles.

• Invent and Simplify: A drive to innovate and find straightforward solutions to complex problems, which is particularly relevant for applied AI solutions.

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

📝 Enhancement Note: Amazon's culture is known for its intensity, customer obsession, and data-driven approach. For a UX Researcher, this means being prepared to defend research findings with data, advocate strongly for the customer, and adapt to fast-paced development cycles. The "Applied AI Solutions" context suggests a focus on B2B or enterprise-level applications, requiring an understanding of business workflows and decision-making processes.

📈 Career & Growth Analysis

Operations Career Level: Senior UX Researcher. This level signifies a seasoned professional capable of independently leading complex, ambiguous research programs, influencing product strategy, and mentoring junior team members. The role demands strategic thinking, strong methodological expertise, and the ability to drive significant impact on product direction and customer outcomes.

Reporting Structure: The Senior UX Researcher will likely report to a User Research Manager or Lead within the AWS Applied AI Solutions organization. They will work closely with senior stakeholders in Product Management, Design, and Engineering, acting as a key research partner for these functions.

Operations Impact: The primary impact of this role is to ensure that AWS Applied AI Solutions are built with a deep understanding of customer needs, leading to higher adoption, greater customer satisfaction, and ultimately, business success for AWS and its clients. By de-risking product decisions and identifying unmet needs, this researcher directly contributes to the efficiency and effectiveness of the product development lifecycle and the overall value proposition of AI-powered business applications.

Growth Opportunities:

• Leadership Development: Progression to Principal UX Researcher or Research Manager roles, leading larger research teams or broader research areas.

• Specialization: Deepening expertise in specific AI domains, enterprise UX, or advanced research methodologies.

• Cross-Functional Influence: Expanding influence across multiple product teams or strategic initiatives within AWS.

• Mentorship: Continued development of mentoring and leadership skills by guiding junior researchers and contributing to team best practices.

• Strategic Impact: Opportunities to shape long-term research strategy and contribute to Amazon's AI innovation roadmap.

📝 Enhancement Note: The "Senior" designation and the context of "Applied AI Solutions" at AWS suggest a career path with significant opportunities for leadership, strategic influence, and specialization within a cutting-edge technology domain. The ability to mentor junior researchers and contribute to broader organizational research practices is a key aspect of growth at this level.

🌐 Work Environment

Office Type: This is an on-site role, implying a traditional office-based work environment. Amazon offices are typically modern, collaborative spaces designed to support team interaction and productivity.

Office Location(s): The role is open to three major tech hubs in the United States: Sunnyvale, California; Seattle, Washington; and New York, New York. These locations offer vibrant ecosystems for tech professionals.

Workspace Context:

• Collaborative Environment: Expect a dynamic workspace that fosters collaboration among UX researchers, designers, product managers, and engineers. Open-plan seating is common, alongside dedicated meeting rooms and focus areas.

• Tools and Technology: Access to industry-standard UX research tools, collaboration platforms (e.g., Slack, Chime), and internal Amazon systems for participant recruitment, data analysis, and project management.

• Team Interaction: Regular opportunities for team meetings, design critiques, research reviews, and cross-functional project discussions. The on-site nature facilitates spontaneous collaboration and knowledge sharing.

Work Schedule: The standard work schedule is typically 40 hours per week. While the role is on-site, there may be some flexibility regarding start and end times, subject to team agreements and project demands. Occasional work outside standard hours may be required for participant recruitment or time-sensitive project deadlines.

📝 Enhancement Note: Being an on-site role in major tech hubs means candidates can expect a professional, well-equipped office environment conducive to collaboration. The emphasis on "Applied AI Solutions" suggests that the teams might be working on complex, fast-paced projects, so a dynamic and potentially demanding work environment is likely.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will review applications and portfolios. Candidates with strong UX research experience, particularly in AI or enterprise software, and a compelling portfolio will be prioritized.

• First Round (Phone/Video): Typically involves a conversation with a UX Researcher or Designer to assess foundational skills, research experience, and cultural fit. Be prepared to discuss your portfolio highlights and specific research projects.

• Subsequent Rounds (On-site/Virtual Loop): A series of interviews with various stakeholders including UX Researchers, Designers, Product Managers, and potentially Engineering or Applied Science leads. These interviews will delve deeper into your experience, methodological expertise, problem-solving abilities, and strategic thinking.

• Portfolio Presentation: A dedicated session where you will present one or more case studies from your portfolio to a panel. This is a critical part of the process, assessing your ability to articulate research problems, methodologies, insights, and impact.

• Final Round: May involve an interview with a senior leader to assess strategic alignment and leadership potential.

Portfolio Review Tips:

• Focus on Impact: Clearly articulate the business problem, your research objectives, the methods you employed, your key findings, and most importantly, the impact of your research on product decisions, user experience, or business outcomes. Use metrics where possible.

• Structure Your Case Studies: Follow a logical narrative: Problem -> Approach -> Process -> Findings -> Recommendations -> Impact.

• Highlight AI/Enterprise Experience: If applicable, emphasize any experience with AI/ML products, enterprise software, or complex business applications. Showcase your understanding of B2B user needs and workflows.

• Demonstrate Methodological Breadth: Showcase your versatility by including examples of different research methods used effectively.

• Prepare for Questions: Anticipate questions about your decision-making process, how you handled challenges, how you collaborated with teams, and how you measured success.

Challenge Preparation:

• Be prepared for a "whiteboarding" or problem-solving exercise, potentially involving a hypothetical research scenario for an AI product. Focus on your process: how you would frame the problem, what questions you would ask, what methods you would consider, and how you would plan the research.

• Practice articulating your thought process clearly and concisely.

• Understand Amazon's Leadership Principles and be ready to provide examples of how you embody them.

📝 Enhancement Note: The interview process at Amazon is rigorous and typically involves multiple stages designed to assess a candidate's skills, experience, and cultural fit. The portfolio presentation is a key component, so candidates must be adept at storytelling and demonstrating research impact. Familiarity with Amazon's Leadership Principles is essential.

🛠 Tools & Technology Stack

Primary Tools:

• Collaboration Platforms: Slack, Amazon Chime, Microsoft Teams (depending on team's internal toolset).

• Prototyping & Design Tools: Figma, Sketch, Adobe XD (for reviewing designs and prototypes).

• Survey Tools: Qualtrics, SurveyMonkey, Google Forms, or internal Amazon tools for survey creation and distribution.

• Data Analysis Software: SPSS, R, Python (for quantitative analysis), NVivo, Dovetail (for qualitative analysis).

Analytics & Reporting:

• Analytics Platforms: Familiarity with web analytics tools (e.g., Google Analytics, Adobe Analytics) and product analytics tools (e.g., Amplitude, Mixpanel) can be beneficial for understanding user behavior data.

• BI Tools: Experience with tools like Tableau, Power BI, or Amazon QuickSight for data visualization and dashboard creation.

CRM & Automation:

• While not direct CRM/automation roles, an understanding of how CRM systems (e.g., Salesforce) and automation platforms impact customer journeys and data collection can be advantageous for context.

📝 Enhancement Note: While the role is UX Research, proficiency with quantitative analysis tools and familiarity with data visualization platforms is increasingly important for demonstrating research impact and connecting insights to business metrics. Experience with enterprise-grade survey and qualitative analysis tools is expected.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: A fundamental principle at Amazon. This role requires a deep commitment to understanding and advocating for the customer's needs, especially in the context of complex AI solutions.

• Ownership: Taking responsibility for research programs, driving them to completion, and ensuring their impact. This includes proactively identifying research opportunities and challenges.

• Invent and Simplify: A drive to innovate new research approaches or simplify complex findings into understandable insights, particularly relevant for cutting-edge AI applications.

• Bias for Action: The ability to move quickly, make decisions with available data, and iterate based on feedback, balancing research rigor with the pace of product development.

• High Standards: Maintaining a high bar for research quality, methodology, and the impact of insights delivered.

Collaboration Style:

• Cross-Functional Partnership: A highly collaborative approach is essential, working closely with Product, Design, Engineering, and Applied Science teams. This involves active listening, clear communication, and a willingness to integrate research findings into diverse workflows.

• Data-Informed Debate: Engaging in constructive discussions based on research evidence and data, aiming to reach the best product decisions for customers and the business.

• Knowledge Sharing: A culture of sharing learnings, best practices, and insights across the team and with broader Amazon research communities.

📝 Enhancement Note: Understanding and embodying Amazon's Leadership Principles is crucial for success and integration within the team culture. The emphasis on customer obsession and data-driven decision-making will shape how research is conducted and presented.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Ambiguity: Leading research in nascent areas of Applied AI where customer needs and market expectations may not be clearly defined. This requires strong problem-framing skills and methodological adaptability.

• Balancing Speed and Rigor: Delivering timely, actionable insights to support rapid product development cycles while maintaining the scientific rigor expected of UX research.

• Influencing Complex Stakeholders: Effectively communicating research findings and advocating for customer needs to senior leaders and cross-functional teams with diverse priorities and perspectives.

• Measuring Impact: Quantifying the impact of UX research on product adoption, customer satisfaction, and business outcomes, especially in the complex domain of AI.

Learning & Development Opportunities:

• AI & ML Domain Expertise: Deepen understanding of AI/ML technologies and their application in business contexts, becoming a subject matter expert.

• Advanced Methodologies: Explore and implement cutting-edge research techniques relevant to AI and complex software.

• Leadership and Mentorship: Develop skills in mentoring junior researchers and contributing to the strategic direction of the UX research practice.

• Cross-Functional Acumen: Gain deeper insights into product management, engineering, and applied science disciplines to enhance collaboration and strategic influence.

📝 Enhancement Note: The primary challenges will revolve around the inherent complexity and rapid evolution of Applied AI, requiring continuous learning and adaptability. Growth opportunities are aligned with advancing expertise, leadership, and strategic influence within a high-impact area of AWS.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you had to conduct research in a highly ambiguous problem space. How did you define the research questions and methodology?" (Focus on your process for framing problems and selecting methods.)

• "How do you ensure your research findings translate into actionable product decisions and influence roadmaps?" (Highlight your communication strategies, synthesis techniques, and stakeholder management.)

• "Walk me through a complex research project where you had to balance speed and rigor. What trade-offs did you make, and what was the outcome?" (Demonstrate your judgment and ability to deliver under pressure.) Company & Culture Questions:

• "How does your approach to UX research align with Amazon's Leadership Principles, particularly Customer Obsession and Bias for Action?" (Prepare specific examples.)

• "How would you approach building trust and credibility with engineering and product teams who may be more focused on technical feasibility or launch timelines?" (Showcase your collaboration and communication skills.)

• "What are your thoughts on using AI to enhance the UX research process itself?" (Demonstrate forward-thinking and alignment with the team's focus.) Portfolio Presentation Strategy:

• Storytelling: Structure your case study presentation as a compelling narrative. Clearly define the problem, your role, your approach, the key findings, and the ultimate impact.

• Data Visualization: Use clear visuals to present data, personas, journey maps, and key insights. Ensure charts and graphs are easy to understand.

• Focus on Impact: Quantify your impact whenever possible. Did your research lead to increased adoption, reduced churn, improved satisfaction scores, or significant cost savings?

• Be Prepared for Deep Dives: Anticipate detailed questions about your methodology, analysis, and decision-making process. Be ready to defend your choices.

• Showcase Collaboration: Highlight how you worked with cross-functional teams and how your research integrated with their work.

📝 Enhancement Note: Candidates should thoroughly research Amazon's Leadership Principles and prepare specific examples from their experience that demonstrate these principles in action. The portfolio presentation is a critical opportunity to showcase not just research skills but also strategic thinking and communication abilities.

📌 Application Steps

To apply for this operations position:

• Submit your application through the official Amazon Jobs portal via the provided URL.

• Customize Your Resume: Tailor your resume to highlight keywords and responsibilities mentioned in the job description, particularly focusing on UX research methodologies, Applied AI, cross-functional collaboration, and leadership experience. Quantify achievements with metrics where possible.

• Curate Your Portfolio: Select 1-3 impactful case studies that best represent your senior-level research capabilities, methodological breadth, and ability to drive product outcomes. Ensure it clearly demonstrates your experience with complex problem spaces and your impact.

• Prepare for the Interview Loop: Review Amazon's Leadership Principles and prepare STAR method (Situation, Task, Action, Result) responses for behavioral questions. Practice articulating your portfolio case studies clearly and concisely.

• Research AWS Applied AI: Gain an understanding of AWS's current AI offerings and the types of business applications the team might be developing. This will help you tailor your responses and demonstrate genuine interest.

⚠️ 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.