Mixed Methods UX Researcher, AI and Compute Enablement

📍 Job Overview

Job Title: Mixed Methods UX Researcher, AI and Compute Enablement Company: Google Location: New York, NY, United States Job Type: Full-time Category: User Experience Research / Product Research Date Posted: September 25, 2025 Experience Level: 6+ Years

🚀 Role Summary

• Conduct mixed-methods user experience research to inform the development of AI and Compute Enablement (ACE) products and platforms.
• Collaborate with cross-functional teams, including Designers, Researchers, Engineers, Content Strategists, and Product Managers, to integrate user insights into the product development lifecycle.
• Employ a range of qualitative and quantitative research methodologies to uncover user needs, behaviors, and pain points.
• Synthesize complex data into actionable insights and compelling narratives to drive product strategy and roadmap decisions.
• Focus on understanding the needs of developers building GenAI systems and the users interacting with Google's AI and compute innovations.

📝 Enhancement Note: This role is positioned within Google's AI and Compute Enablement (ACE) team, specifically focusing on next-generation AI and compute innovations. The emphasis on "developers who build them" for GenAI systems indicates a B2B or developer-facing product research focus, requiring an understanding of developer workflows, pain points, and needs within the AI/ML ecosystem. The "user-centricity" aspect suggests a strong emphasis on understanding how developers and potentially end-users interact with and benefit from these advanced AI/compute platforms.

📈 Primary Responsibilities

• Identify and prioritize high-impact research opportunities that align with product, design, and business objectives to define and influence strategy and roadmaps for MSCA products and solutions.
• Scope and execute foundational, exploratory, and evaluative research utilizing qualitative and quantitative methods including surveys, interviews, usability studies, contextual inquiry, and log analysis to answer product questions, improve customer pain points, and drive business value.
• Synthesize diverse data streams into compelling insights, actionable reports, and persuasive presentations that inspire adoption, engagement, and expansion of user-centricity, delivering critical business intelligence to cross-functional partners, including executive cross-disciplinary leaders.
• Engage in rapid research cycles, design facilitation, stakeholder alignment sessions, and concept validation to solve user problems and contribute to scaling user-centric solutions within the AI and Compute domain.
• Build strong, collaborative partnerships with peers and stakeholders across product, engineering, and design, effectively incorporating technical and business domain expertise into research plans and findings, and leveraging user data as a strategic tool for navigating ambiguity.

📝 Enhancement Note: The responsibilities highlight a strategic research role, requiring not just execution but also the ability to identify research opportunities, influence roadmaps, and communicate insights to executive leadership. The mention of "rapid research" and "design facilitation" suggests a dynamic environment where research is tightly integrated with fast-paced product development cycles. The focus on "developer pain points" and "GenAI systems" implies a need for research that can delve into technical workflows and the specific challenges faced by AI developers.

🎓 Skills & Qualifications

Education:

• Bachelor's degree in Human-Computer Interaction, Cognitive Science, Computer Science, Psychology, Anthropology, Statistics, or a related field, or equivalent practical experience.

Experience:

• Minimum of 6 years of experience in an applied research setting, such as product research within a technology company or academic research with direct application to product development.
• Proven track record of effectively collaborating with multidisciplinary teams (e.g., Designers, Researchers, Engineers, Content Strategists, and Product Managers) throughout the entire product development process, from ideation to launch and iteration.

Required Skills:

• Expertise in a wide range of research methodologies, including but not limited to: usability studies, contextual inquiry, one-to-one interviews, diary studies, surveys, field studies, and log analysis.
• Demonstrated ability to synthesize qualitative and quantitative data into clear, actionable insights and compelling narratives that drive product decisions and business value.
• Strong communication and presentation skills, with the ability to effectively articulate research findings and recommendations to diverse audiences, including technical teams and executive leadership.
• Experience in problem-solving and navigating ambiguous environments, requiring analytical thinking and a proactive, adaptable approach.
• Proficiency in identifying and prioritizing research opportunities that align with strategic business and product objectives.

Preferred Skills:

• Master's degree or PhD in Human-Computer Interaction, Cognitive Science, Psychology, Anthropology, Statistics, or a related field.
• Experience in front-end development, technical UX design, or prototyping, providing a deeper understanding of the technical product landscape.
• Knowledge and passion for AI/ML academic research or development, with a specific interest in Generative AI (GenAI) systems and the developer community that builds them.
• Ability to operate with high flexibility and apply a strategic problem-solving attitude in dynamic and often ambiguous environments.

📝 Enhancement Note: The preferred skills suggest that candidates with advanced degrees or specific technical UX experience will be highly regarded. A strong understanding of AI/ML, particularly GenAI, is a significant advantage, indicating that the role requires a researcher who can grasp complex technical concepts and their implications for user experience. The emphasis on "developer experience" for AI/ML platforms is a key differentiator.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase a diverse range of user research projects, clearly demonstrating the application of mixed methods (qualitative and quantitative) to solve complex user problems.
• Include case studies that highlight your ability to identify high-impact research opportunities and influence product strategy and roadmaps.
• Provide evidence of your skill in synthesizing data into compelling insights, reports, and presentations that have driven tangible product improvements or business value.
• Demonstrate experience with rapid research cycles, design facilitation, and stakeholder alignment within product development lifecycles.
• Highlight projects where you've collaborated effectively with multidisciplinary teams (design, engineering, product management) to integrate user feedback throughout product development.

Process Documentation:

• Present clear documentation of your research processes, from initial scoping and methodology selection through data analysis, synthesis, and reporting.
• Include examples of how you've translated user insights into actionable recommendations and how these recommendations were integrated into product development.
• Showcase your approach to managing research projects, including timelines, stakeholder communication, and managing ambiguity.
• If applicable, include examples of how you've utilized or contributed to research tools, platforms, or frameworks.

📝 Enhancement Note: For this role, a portfolio should emphasize research projects that led to concrete product improvements or strategic shifts, particularly within technical or developer-focused products. Demonstrating the ability to handle ambiguity and work with complex technical concepts (like AI/ML) is crucial. The portfolio should clearly articulate the research process, the methodologies used, and the impact of the research on the product and business.

💵 Compensation & Benefits

Salary Range: $151,000 - $222,000 per year (US base salary)

Benefits:

• Bonus eligibility
• Equity participation
• Comprehensive benefits package (details typically include health insurance, retirement plans, paid time off, etc.)

Working Hours: Standard full-time hours, likely around 40 hours per week, with potential for flexibility depending on project needs and team dynamics.

📝 Enhancement Note: The provided salary range is for base salary only and does not include additional compensation such as bonuses or equity, which are common at Google. The role is specified as full-time. Given Google's global presence and emphasis on work-life balance, while the role is on-site, there may be an expectation of flexibility for research activities and team collaboration.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology, Artificial Intelligence, Compute Infrastructure, Software Development. Company Size: Google is a large, global technology company, employing hundreds of thousands of people worldwide. This scale offers vast resources, opportunities for specialization, and exposure to cutting-edge technologies. Founded: 1998. Google has a long history of innovation, focusing on user experience, data-driven decision-making, and building scalable technologies.

Team Structure:

• The User Experience (UX) team at Google is multidisciplinary, comprising UX Designers, Researchers, Writers, Content Strategists, Program Managers, and Engineers.
• The AI and Compute Enablement (ACE) team is part of a larger organizational structure (MSCA), focusing on next-gen AI and compute innovations.
• Researchers work closely with product managers and engineers, integrating into product development teams to provide user insights.

Methodology:

• Google emphasizes a user-centric approach, with the philosophy "Focus on the user and all else will follow."
• Research is deeply integrated into the product development lifecycle, utilizing a mix of qualitative and quantitative methods.
• Data analysis and synthesis are key, translating complex user behaviors and needs into actionable product strategies.
• Collaboration and knowledge sharing are encouraged through internal research communities, meetups, and mentorship programs.

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

📝 Enhancement Note: Google's culture is known for its emphasis on innovation, data-driven decision-making, and a strong focus on the user. The UX team operates collaboratively within product development cycles. The ACE team's focus on AI and compute implies working at the forefront of technological advancement, requiring adaptability and a continuous learning mindset.

📈 Career & Growth Analysis

Operations Career Level: This role is for an experienced Mixed Methods UX Researcher, typically falling into an individual contributor (IC) role at a mid-to-senior level, depending on the specific Google leveling structure (e.g., L4-L6). It requires significant autonomy and the ability to influence product strategy. Reporting Structure: UX Researchers typically report into a UX Research lead or manager, and work closely with product managers and engineering leads within specific product areas. They are embedded within product teams. Operations Impact: UX Researchers at Google have a direct impact on product strategy, design, and development by ensuring user needs are understood and addressed. Their insights directly influence the usability, desirability, and overall success of products, including complex AI and compute platforms, impacting both developer experience and end-user satisfaction.

Growth Opportunities:

• Skill Specialization: Deepen expertise in specific research methodologies, AI/ML user experience, or developer research.
• Leadership: Transition into lead research roles, mentoring junior researchers, or taking ownership of larger research initiatives and areas.
• Cross-functional Mobility: Move into related roles such as Product Management, UX Design, or Program Management, leveraging research expertise.
• Learning & Development: Access to Google's extensive internal training programs, research communities, and opportunities to learn from leading experts in UX and AI.

📝 Enhancement Note: As an experienced researcher at Google, the growth path typically involves increasing scope of influence, mentorship opportunities, and potential specialization in high-demand areas like AI/ML user experience or developer research. The role offers significant opportunities to shape product direction and contribute to Google's core technological advancements.

🌐 Work Environment

Office Type: This is an on-site role, indicating a traditional office environment where collaboration and in-person interaction are expected. Office Location(s): New York, NY. This location likely offers a vibrant tech ecosystem and a collaborative office space.

Workspace Context:

• The workspace is designed to foster collaboration among multidisciplinary teams. Expect to work closely with designers, product managers, and engineers in shared spaces.
• Access to Google's internal research tools, labs, and resources will be available to support research activities.
• The environment likely encourages innovation, experimentation, and a data-driven approach to problem-solving.

Work Schedule: While typically adhering to standard business hours (e.g., 40 hours/week), Google often emphasizes flexibility to accommodate project needs and work-life balance. Researchers may need to adapt schedules for user interviews or global team collaborations.

📝 Enhancement Note: The on-site requirement in New York suggests an environment conducive to team collaboration and access to Google's extensive office amenities. The emphasis on cross-functional work means the workspace will likely support dynamic team interactions and project-based work.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: Recruiter screens for minimum qualifications and basic fit.
• Hiring Manager Interview: Focus on experience, research philosophy, and alignment with team needs.
• Research Portfolio Review: A critical stage where candidates present 2-3 in-depth case studies showcasing their research process, methodologies, insights, and impact. Expect detailed questions about your role, challenges, and outcomes.
• On-site/Virtual Interviews: Typically include interviews with other UX Researchers, Designers, Product Managers, and Engineers. These sessions will assess collaboration skills, problem-solving abilities, technical understanding (especially related to AI/ML), and cultural fit.
• Potential Research Exercise: Some roles may involve a take-home assignment or a live research exercise to evaluate practical skills.

Portfolio Review Tips:

• Structure Your Case Studies: For each project, clearly outline the problem, your role, the research questions, methodologies used, your specific contributions, key findings, and the impact on the product or business (quantify where possible).
• Highlight Mixed Methods: Explicitly showcase how you combined qualitative and quantitative approaches to gain a comprehensive understanding.
• Demonstrate Influence: Show instances where your research directly influenced product decisions, design iterations, or strategic direction.
• Address Ambiguity: Be prepared to discuss how you tackled ambiguous problems or evolving requirements.
• Technical Acumen: For this role, be ready to discuss your understanding of AI/ML concepts and how you would approach researching developer tools or AI systems.
• Conciseness and Clarity: Present your work clearly and concisely, focusing on the most impactful aspects.

Challenge Preparation:

• Research Scenarios: Anticipate questions about how you would approach research for specific AI/compute products or developer tools. Think about potential user groups, research questions, and methodologies.
• Collaboration Scenarios: Prepare examples of how you've successfully collaborated with engineers, designers, and PMs, especially when navigating technical complexities or disagreements.
• Problem-Solving: Be ready to walk through a complex problem you solved using research, detailing your thought process and the outcome.
• AI/ML Knowledge: Brush up on current trends and challenges in AI/ML development and user experience.

📝 Enhancement Note: The portfolio review is a cornerstone of the Google UX Research hiring process. Candidates must be prepared to deeply articulate their research process, decision-making, and the impact of their work. Demonstrating an understanding of technical domains, especially AI/ML and developer tools, will be crucial for this specific role.

🛠 Tools & Technology Stack

Primary Tools:

• Research Platforms: Google likely utilizes proprietary internal tools for participant recruitment, survey deployment, usability testing, and data analysis. Familiarity with general survey tools (e.g., Qualtrics, SurveyMonkey) and usability testing platforms (e.g., UserTesting.com, Lookback) is beneficial.
• Data Analysis Tools: Proficiency in statistical software (e.g., R, SPSS) or data analysis platforms (e.g., Python with libraries like Pandas, NumPy) for quantitative data is advantageous. Qualitative analysis tools (e.g., Dovetail, NVivo) may also be used.
• Collaboration Tools: Google Workspace (Docs, Sheets, Slides, Meet) for documentation, collaboration, and presentations. Project management tools like Jira or Asana may also be integrated.

Analytics & Reporting:

• Data Visualization: Tools like Tableau, Looker, or advanced Excel/Google Sheets for creating insightful reports and dashboards.
• User Data Analysis: Understanding how to work with product analytics data (e.g., Google Analytics, Amplitude) to complement primary research.

CRM & Automation:

• While not directly a CRM role, understanding how user data is managed and how research insights feed into product iterations is valuable. Familiarity with workflow automation concepts can be helpful for understanding developer tools.

📝 Enhancement Note: While specific tools are often proprietary at Google, a strong foundation in general research tooling, data analysis software (both qualitative and quantitative), and collaboration platforms is essential. Candidates should be comfortable learning new tools quickly and adapting their skills to Google's internal ecosystem.

👥 Team Culture & Values

Operations Values:

• User Focus: A deep commitment to understanding and advocating for the user in all product decisions.
• Data-Driven: Reliance on rigorous data analysis and synthesis to inform strategy and validate hypotheses.
• Collaboration: Working effectively with diverse teams to achieve shared goals.
• Innovation: Encouraging experimentation, pushing boundaries, and seeking novel solutions.
• Impact: Focusing on delivering meaningful improvements and measurable business value.

Collaboration Style:

• Highly collaborative, with researchers embedded within product teams.
• Open communication and knowledge sharing are encouraged through team meetings, internal forums, and research communities.
• Cross-functional partners (PMs, Eng, Design) are treated as integral team members in the research process.
• Feedback is actively sought and given to foster continuous improvement.

📝 Enhancement Note: Google's culture values individuals who are proactive, collaborative, and driven by a desire to make a significant impact. For this role, aligning with a user-centric and data-driven approach, while embracing the innovative nature of AI/ML development, will be key to cultural fit.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of AI/ML Systems: Researching cutting-edge AI and compute technologies requires staying abreast of rapid advancements and understanding complex technical concepts.
• Developer Experience Nuances: Understanding the specific needs, workflows, and pain points of developers building AI/ML systems can be challenging and requires domain expertise.
• Ambiguity in New Product Areas: Working on next-generation innovations often means navigating undefined user needs and market landscapes.
• Balancing Research Rigor with Speed: Delivering timely insights in a fast-paced product development environment requires efficient research planning and execution.

Learning & Development Opportunities:

• AI/ML Domain Expertise: Opportunities to deepen knowledge in AI/ML research and the developer ecosystem.
• Methodological Advancement: Access to training and mentorship to refine and expand research skills across a wide spectrum of methods.
• Cross-Functional Exposure: Learning from experts in design, engineering, and product management to gain a holistic understanding of product development.
• Industry Engagement: Potential to attend conferences, publish research, and contribute to the broader UX and AI research communities.

📝 Enhancement Note: The primary challenge lies in bridging the gap between complex technology and user needs, particularly for a developer audience. The growth opportunities are substantial, offering the chance to become a subject matter expert in a rapidly evolving field and influence the future of Google's AI initiatives.

💡 Interview Preparation

Strategy Questions:

• "How would you approach researching the usability of a new AI model for developers who need to integrate it into their applications?"
• "Describe a time you had to influence a product roadmap with research findings. What was the situation, your approach, and the outcome?"
• "Walk me through a complex user problem you solved through research. What were the key insights, and how did they lead to a solution?"
• "How do you stay current with advancements in AI/ML research and their implications for user experience?"

Company & Culture Questions:

• "What interests you about Google's AI and Compute Enablement team specifically?"
• "How do you typically collaborate with engineers and product managers who may have different priorities or perspectives?"
• "Describe your process for synthesizing findings from multiple research studies to create a cohesive narrative."

Portfolio Presentation Strategy:

• Storytelling: Frame your case studies as narratives with a clear beginning (the problem), middle (your research process and findings), and end (the impact and learnings).
• Quantify Impact: Wherever possible, use metrics to demonstrate the impact of your research (e.g., "reduced task completion time by X%", "increased user satisfaction by Y%", "influenced feature prioritization leading to Z% adoption").
• Show Your Process: Clearly articulate your thinking behind methodology choices, data analysis, and synthesis.
• Engage Your Audience: Be prepared for in-depth questions and foster a dialogue rather than just a presentation.

📝 Enhancement Note: Preparation should focus on demonstrating a deep understanding of research methodologies, the ability to translate complex technical concepts into user insights, and a proven track record of influencing product decisions. Highlighting experience with AI/ML or developer tools will be a significant advantage.

📌 Application Steps

To apply for this Mixed Methods UX Researcher position:

• Submit your application through the official Google Careers portal.
• Curate Your Portfolio: Select 2-3 of your most impactful research projects that best showcase your mixed-methods expertise, ability to influence product strategy, and experience with complex or technical domains. Tailor your presentation to highlight AI/ML or developer research if applicable.
• Tailor Your Resume: Ensure your resume clearly outlines your experience with various research methodologies, your collaboration skills, and any specific experience related to AI/ML, developer tools, or technical products. Use keywords from the job description.
• Practice Your Presentation: Rehearse your portfolio walkthrough, focusing on clear communication, concise explanations of your process, and quantifiable impact. Be prepared to answer detailed questions about your contributions and decision-making.
• Research Google's UX: Familiarize yourself with Google's approach to UX research, their product philosophy, and recent developments in their AI and Compute Enablement initiatives to better align 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.