Senior UX Designer, AI Systems Knowledge and Intelligence Engine

📍 Job Overview

Job Title: Senior UX Designer, AI Systems Knowledge and Intelligence Engine

Company: Google

Location: Kirkland, WA; New York, NY; San Jose, CA

Job Type: Full-time

Category: User Experience (UX) Design / AI Systems Design

Date Posted: 2026-08-26

Experience Level: Mid-Senior Level (5-10 years)

Remote Status: On-site

🚀 Role Summary

• This role focuses on the design of AI systems, specifically the knowledge and intelligence engines that power Google's agentic ecosystem, requiring a blend of user-centered design and AI fluency.

• The Senior UX Designer will be responsible for translating complex data into actionable narratives and designing intuitive interfaces for both individual contributors and executives, enhancing the perception and efficiency of AI tools.

• A key aspect of this position involves deep collaboration with cross-functional teams, including UX researchers, software engineers, and product managers, to conceptualize and deliver end-to-end AI-powered design solutions.

• The role demands an "AI builder mindset" and an iterative approach to improve Google's velocity and innovation in AI development, contributing to the core technical foundations of Google's products.

📝 Enhancement Note: The title "Senior UX Designer, AI Systems Knowledge and Intelligence Engine" strongly indicates a specialized role within UX, focusing on the backend intelligence and knowledge management systems that support AI agents and products. This is distinct from a typical front-end or user-facing product UX role, requiring a deeper understanding of AI architecture, data flows, and system-level design. The emphasis on "agentic ecosystem" and "intelligence engine" suggests a focus on how AI agents interact with knowledge bases and how this interaction is designed from a system perspective, not just an end-user interface perspective. This implies a need for candidates who can think abstractly about system design and data representation.

📈 Primary Responsibilities

• Design and develop the foundational intelligence layer for Google's agentic ecosystem, focusing on knowledge representation, retrieval, and feedback loops.

• Apply AI-fluent, user-centered design principles to create end-to-end solutions, encompassing technical discovery, system design, and final asset creation.

• Transform complex qualitative and quantitative data into rich, actionable narratives and interactive dashboards to support executive decision-making and drive strategic insights.

• Collaborate closely with UX researchers, software engineers, and product managers to translate intricate technical constraints and AI capabilities into seamless, efficient user experiences.

• Design intuitive tools and systems that cater to the distinct information needs and interaction patterns of individual contributors (e.g., developers, AI trainers) and executive stakeholders.

• Develop seamless onboarding experiences and compelling data narratives that enhance the understanding, adoption, and overall efficiency of AI tools across Google.

• Contribute to the evolution of the Google design language, ensuring consistency and innovation in AI system design across various products.

• Utilize prototyping skills to iterate rapidly on system designs, gather feedback, and effectively communicate technical concepts to engineering teams.

📝 Enhancement Note: Given the focus on "AI Systems Knowledge and Intelligence Engine" and "agentic ecosystem," the responsibilities lean heavily into system design, data architecture visualization, and the design of interfaces for complex AI interactions rather than purely consumer-facing product design. The mention of designing for "individual contributors and executives" highlights the need for tiered information design and tailored user journeys within these AI systems. The emphasis on transforming data into "actionable narratives and dashboards" suggests a strong requirement for data visualization and information architecture skills applied to complex AI outputs and system performance metrics.

🎓 Skills & Qualifications

Education:

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

• Master's degree in a relevant field is preferred, indicating a desire for advanced theoretical understanding and research capabilities. Experience:

• Minimum of 6 years of visual design experience in product design or UX/UI design.

• Experience incorporating generative AI, large language model (LLM) APIs, and agentic workflows into user experiences.

• Demonstrated experience in using data to tell compelling stories through interactive media and visualizations.

• Proven ability to apply design frameworks to align design outcomes with organizational goals and performance standards.

• Experience with technical prototyping, such as HTML/CSS/JS, to facilitate effective collaboration with engineering teams.

• Experience in system design and the ability to balance system design with a prototyping approach. Required Skills:

• Visual Design & Product Design: 6+ years of experience crafting visually appealing and functional user interfaces for products.

• UX/UI Design: Expertise in user experience principles and user interface design best practices.

• Data Visualization: Proficient in translating complex datasets into clear, insightful, and actionable visual representations.

• User-Centered Design: Strong adherence to user-centered design methodologies, ensuring solutions meet user needs and business objectives.

• Cross-functional Collaboration: Proven ability to work effectively with UX researchers, software engineers, and product managers in a multidisciplinary environment.

Preferred Skills:

• Generative AI & LLMs: Experience in designing user experiences that leverage generative AI capabilities and LLM APIs.

• Agentic Workflows: Understanding and experience in designing interactions for autonomous agents or AI systems with agentic behaviors.

• Data Storytelling: Ability to craft compelling narratives from data, making complex information accessible and impactful.

• System Design: Experience in designing complex systems, understanding their architecture, data flows, and feedback mechanisms.

• Technical Prototyping (HTML/CSS/JS): Familiarity with front-end development technologies to create interactive prototypes and communicate effectively with engineers.

• Design Frameworks & Goal Alignment: Experience applying structured approaches to ensure design initiatives contribute to broader organizational objectives.

📝 Enhancement Note: The "Preferred Qualifications" section is critical here, highlighting a significant shift towards AI-specific design skills. Experience with generative AI, LLMs, and agentic workflows is not just a plus but a core requirement for success in this specialized role. The emphasis on "system design" and "technical discovery" suggests that this role is less about surface-level UI polish and more about the underlying architecture and logic of AI systems. The mention of "AI builder mindset" implies a proactive, experimental approach to leveraging AI capabilities.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies on AI System Design: Showcase projects where you designed complex AI systems, intelligence engines, or agentic workflows, detailing the problem, your design process, and the implemented solution.

• Data Visualization & Narrative Examples: Include examples of how you've translated complex data into compelling visual narratives, dashboards, or interactive reports, demonstrating your ability to drive insights and decision-making.

• User-Centered AI Design: Present projects that clearly articulate how user needs were identified and met through AI-powered solutions, detailing user research methodologies and validation processes.

• Cross-Functional Collaboration Evidence: Highlight projects where you collaborated effectively with engineering, research, and product management to bring AI designs to fruition, showcasing your communication and integration skills.

• Prototyping & Technical Understanding: Demonstrate proficiency in prototyping tools and, if applicable, your ability to create technical prototypes (e.g., HTML/CSS/JS) to illustrate system behavior or complex interactions.

Process Documentation:

• Workflow Design for AI Systems: Detail your process for mapping out complex AI workflows, including data ingestion, processing, decision-making logic, and output generation.

• Iterative Design & Feedback Loops: Showcase how you establish and utilize feedback loops (user testing, A/B testing, system performance data) to iterate on AI system designs and improve outcomes.

• System Performance Metrics & Analysis: Provide examples of how you define, track, and analyze key performance indicators (KPIs) for AI systems and their associated user experiences.

• Design Framework Application: Document instances where you applied specific design frameworks or methodologies to align AI system design with organizational goals and measure its impact.

📝 Enhancement Note: The portfolio requirements for this role are highly specific to AI systems and data-driven design. Generic UX portfolios will likely not suffice. Applicants must clearly demonstrate their ability to design not just interfaces, but the underlying intelligence and knowledge structures that power AI. Emphasis should be placed on how design decisions impact system performance, data interpretation, and the efficiency of AI-driven processes. The ability to articulate and visualize complex system logic is paramount.

💵 Compensation & Benefits

Salary Range:

• US: $159,000 - $230,000 USD per year. This range reflects a senior-level position at a leading technology company, accounting for the specialized skills in AI systems design and the competitive market for such talent. The specific salary within this range will be determined by factors such as the candidate's experience level, specific expertise in AI, and performance during the interview process.

Benefits:

• Bonus Target: A performance-based bonus target, typically a percentage of base salary, reflecting Google's incentive structure for high-achieving employees.

• Equity: Stock options or grants, providing employees with ownership in the company's growth and success.

• Health Insurance: Comprehensive medical, dental, and vision insurance plans.

• Retirement Benefits: Plans such as a 401(k) with company match.

• Additional Benefits: Access to Google's renowned campus amenities, professional development programs, wellness initiatives, and employee assistance programs.

Working Hours:

• Standard full-time hours are approximately 40 hours per week. However, the nature of a Senior UX Designer role, particularly in AI development, may require flexibility to meet project deadlines and collaborate across time zones.

📝 Enhancement Note: The salary range provided ($159,000 - $230,000 USD) is directly from the job posting and aligns with industry benchmarks for Senior UX Designers at major tech companies in high cost-of-living areas like Kirkland, New York, and San Jose. The inclusion of a bonus target and equity indicates a comprehensive compensation package typical for senior roles at companies like Google. The mention of "AI builder mindset" and "iterative team focused on improving Google's velocity" suggests that while standard hours apply, a proactive and dedicated approach is expected.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Software Development, Artificial Intelligence, Internet Services)

Company Size: Large (10,000+ employees). Google's vast scale offers immense resources, opportunities for impact across global products, and a highly structured yet innovative work environment.

Founded: 1998. With over two decades of innovation, Google has a deeply ingrained culture of data-driven decision-making, user-centricity, and pushing technological boundaries.

Team Structure:

• Core Team Focus: This role is within the "Core team," which builds the technical foundation, developer platforms, product components, and infrastructure for Google's flagship products. This implies a highly technical and foundational aspect to the work.

• Multi-disciplinary UX Team: The Senior UX Designer will join a multi-disciplinary UX team, collaborating closely with UX researchers, software engineers, and product managers.

• Reporting Structure: Likely reports into a UX leadership role within the Core team or a specific AI/Intelligence Engine product group, with close alignment to engineering and product management leads.

Methodology:

• User-Centered Design: Google's foundational principle: "Focus on the user and all else will follow." This guides all design decisions.

• Data-Driven Decision-Making: Heavy reliance on user data, performance metrics, and A/B testing to inform design choices and measure impact.

• Iterative Development: An agile and iterative approach to design and development, especially within AI, to adapt to rapid technological advancements and user feedback.

• AI-Fluent Design: Integrating an "AI builder mindset" to leverage AI capabilities effectively and creatively.

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

📝 Enhancement Note: Being part of Google's "Core team" signifies working on foundational elements that impact numerous products. This is a high-impact role where design decisions have broad reach. The culture emphasizes rigorous data analysis, user empathy, and continuous innovation, particularly in cutting-edge areas like AI. The collaborative environment is structured, with clear roles and responsibilities but a strong emphasis on cross-functional teamwork.

📈 Career & Growth Analysis

Operations Career Level: Senior UX Designer. This level signifies a high degree of autonomy, expertise, and the ability to lead complex design initiatives. It involves not just execution but also strategic input, mentorship, and influencing design direction.

Reporting Structure: The role reports into a UX leadership position within the Core team, working closely with Engineering and Product Management leads. This structure emphasizes cross-functional collaboration and shared ownership of product strategy and execution.

Operations Impact: The Senior UX Designer will have a significant impact on Google's AI capabilities by shaping the intelligence engines and system feedback loops. This role directly influences how developers build with AI, how autonomous agents function, and how complex data is translated into actionable insights for decision-makers across the company. The work contributes to the overall technical foundation and innovation pace of Google's core products.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI systems design, generative AI, LLMs, and agentic workflows, becoming a go-to expert in these cutting-edge fields.

• Leadership & Mentorship: Opportunity to mentor junior designers, lead design initiatives, and contribute to the strategic direction of AI product development within the Core team.

• Cross-Product Impact: Gain experience working on foundational elements that span multiple Google products, offering broad exposure and understanding of Google's technological ecosystem.

• Advance to Principal/Staff Roles: Potential progression to Principal UX Designer or Staff UX Designer roles, focusing on highly complex, strategic, and company-wide initiatives.

• Transition to Product Management or Engineering Leadership: The blend of technical understanding and design strategy can open doors to related leadership roles in product management or engineering.

📝 Enhancement Note: This role represents a significant step up in specialization and impact within the UX field, particularly in the rapidly evolving domain of AI. The "Senior" title implies expectations of leadership, strategic thinking, and the ability to mentor others. Growth is framed around deepening AI expertise and potentially moving into more strategic or leadership-focused design roles within Google's core technology divisions.

🌐 Work Environment

Office Type: On-site. Google is known for its state-of-the-art office spaces designed to foster collaboration, innovation, and employee well-being. This role is specified as on-site, requiring regular presence in one of the designated office locations.

Office Location(s):

• Kirkland, Washington

• New York, New York

• San Jose, California

These locations are major tech hubs, offering vibrant work environments and access to a diverse talent pool.

Workspace Context:

• Collaborative Spaces: Offices are equipped with various meeting rooms, brainstorming areas, and open-plan spaces designed to encourage spontaneous interaction and teamwork.

• Technology & Tools: Access to Google's internal suite of design, development, and collaboration tools, along with high-performance computing resources necessary for AI-related work.

• Cross-functional Interaction: The work environment is structured to facilitate regular interaction with UX researchers, software engineers, product managers, and potentially AI researchers, fostering a dynamic exchange of ideas.

• Amenities: Google offices typically offer extensive amenities such as cafes, fitness centers, relaxation areas, and more, contributing to a positive and productive work atmosphere.

Work Schedule:

• Standard full-time (approximately 40 hours per week). While specific hours may vary, the on-site nature implies a structured work day within the office. The dynamic nature of AI development may necessitate occasional flexibility to meet project milestones.

📝 Enhancement Note: The on-site requirement suggests a preference for in-person collaboration, which is often beneficial for complex, multi-disciplinary projects like AI system design. Google's office environments are designed to support this, offering a blend of focused work areas and collaborative zones. The choice of major tech hubs as locations indicates access to significant industry resources and talent.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A review of your resume and portfolio to assess qualifications and experience against the job requirements, with a strong emphasis on AI systems design and data visualization.

• Recruiter Screen: A call with a recruiter to discuss your background, interest in the role, and initial fit with Google's culture and the team's needs.

• Design Portfolio Review: A dedicated session where you will present your portfolio, focusing on case studies that demonstrate your expertise in AI systems, data storytelling, and user-centered design for complex technical products. Expect to discuss your design process, decision-making rationale, and impact.

• On-site/Virtual Interviews (Multiple Rounds):

  • UX/Design Focused Interviews: Deep dives into your design process, problem-solving abilities, and how you approach user research, ideation, prototyping, and iteration for AI systems.
  • Cross-functional Collaboration Interviews: Discussions with potential team members (Engineers, Product Managers, Researchers) to assess your ability to collaborate effectively, communicate technical concepts, and integrate design into broader product development cycles.
  • System Design & AI Fluency Interview: A session likely focused on your understanding of AI systems, LLMs, agentic workflows, and how you translate these technical concepts into user experiences. This may involve discussing technical constraints and opportunities.
  • Leadership & Mentorship Interview: For a Senior role, expect questions assessing your leadership potential, ability to mentor others, and strategic thinking.

• Final Round/Hiring Committee Review: A comprehensive review of all feedback, often culminating in a decision by a hiring committee.

Portfolio Review Tips:

• Curate for AI Systems: Select 3-4 of your strongest projects that specifically showcase your experience with AI systems, knowledge engines, LLMs, data visualization, and system design. Generic UI design projects may not be sufficient unless they have a strong data or AI component.

• Tell a Story: For each case study, clearly articulate the problem, your role, the design process, the challenges faced, the solutions you developed (especially system-level ones), and the measurable impact or outcomes.

• Highlight AI Fluency: Explicitly discuss how you incorporated AI capabilities, managed technical constraints, and designed for complex data or agentic interactions. Use terminology like "LLM APIs," "generative AI," "agentic workflows," and "intelligence engine" where appropriate.

• Showcase Data Visualization: Dedicate a portion of your presentation to how you transform complex data into actionable insights and narratives, demonstrating your dashboard design and data storytelling skills.

• Demonstrate Collaboration: Explain how you worked with engineers, researchers, and PMs, and how your design decisions were informed by their input and technical feasibility.

Challenge Preparation:

• System Design Challenge: Be prepared for a hypothetical challenge related to designing an AI system, an intelligence engine, or a user experience for a complex AI tool. Focus on defining user needs, outlining system architecture, data flows, and key interaction points.

• Data Interpretation & Visualization: Practice how you would analyze a given dataset and propose a visualization strategy or dashboard design to communicate key insights effectively.

• AI Concept Explanation: Be ready to explain complex AI concepts (like LLMs or agentic workflows) in simple terms and discuss their potential UX implications.

• Behavioral Questions: Prepare examples using the STAR method (Situation, Task, Action, Result) for questions related to leadership, problem-solving, collaboration, handling ambiguity, and dealing with technical challenges.

📝 Enhancement Note: The interview process at Google is rigorous and multi-faceted. For this specific role, the emphasis will be on the candidate's ability to bridge the gap between complex AI technology and user experience, particularly at a system level. The portfolio is crucial and must be tailored to showcase AI-specific design thinking and data narrative skills.

🛠 Tools & Technology Stack

Primary Tools:

• Design & Prototyping Software: Figma, Sketch, Adobe Creative Suite (Illustrator, Photoshop), possibly specialized tools for system diagramming or information architecture.

• Prototyping Tools: Protopie, Framer, or custom code (HTML/CSS/JS) for interactive prototypes.

• Collaboration Tools: Google Workspace (Docs, Sheets, Slides, Meet), Jira, Confluence.

Analytics & Reporting:

• Data Visualization Tools: Tableau, Looker (Google's own BI platform), Power BI, or custom dashboarding solutions.

• Analytics Platforms: Google Analytics, internal Google analytics tools for user behavior tracking and performance analysis.

• Data Querying (Basic): Familiarity with SQL or similar query languages may be beneficial for understanding data sources.

CRM & Automation:

• While not a direct CRM role, understanding how user data flows from various sources (potentially including CRM-like systems) into AI models and reporting dashboards is important. Experience with data pipelines and integration concepts may be a plus.

📝 Enhancement Note: Proficiency in industry-standard design tools like Figma and Adobe Creative Suite is expected. The role's focus on AI systems and data visualization implies a need for experience with data visualization tools and potentially basic data querying. Familiarity with Google's internal ecosystem (Google Workspace, Looker) would be a significant advantage. The ability to prototype using code (HTML/CSS/JS) is specifically called out as preferred, highlighting the technical nature of the role.

👥 Team Culture & Values

Operations Values:

• User Focus: A deep commitment to understanding and serving user needs, even when designing complex AI systems. The principle "Focus on the user and all else will follow" is paramount.

• Data-Driven Approach: Decisions are grounded in data, metrics, and user research. A strong emphasis on measuring impact and iterating based on evidence.

• Innovation & Speed: A culture that encourages pushing boundaries, experimenting with new technologies (especially AI), and iterating quickly to deliver value. An "AI builder mindset" is key.

• Collaboration & Teamwork: Strong emphasis on cross-functional partnership, open communication, and shared ownership of goals. Working effectively with engineers, researchers, and product managers is essential.

• Excellence & Quality: A commitment to producing high-quality, reliable, and scalable solutions that form the foundational elements of Google's products.

Collaboration Style:

• Cross-functional Integration: The role is embedded within a multi-disciplinary team, requiring constant collaboration with engineering, product management, and UX research. Expect regular syncs, joint problem-solving sessions, and shared responsibility for outcomes.

• Iterative Feedback: A culture of continuous feedback, where designs are shared early and often, and input is actively sought from all team members to refine solutions.

• Knowledge Sharing: Encouragement to share learnings, best practices, and insights, particularly around AI systems design and data storytelling, to elevate the entire team's capabilities.

📝 Enhancement Note: Google's culture is characterized by a blend of ambitious goals, rigorous analysis, and a strong sense of collective ownership. For this role, the emphasis on "AI builder mindset" and "improving Google's velocity" suggests a proactive, experimental, and results-oriented approach within a highly collaborative framework.

⚡ Challenges & Growth Opportunities

Challenges:

• Designing for AI Complexity: Translating highly technical AI concepts (LLMs, agentic workflows, knowledge graphs) into intuitive and effective user experiences for diverse audiences (developers, executives).

• Data Storytelling at Scale: Effectively visualizing and narrating complex, multi-dimensional data generated by AI systems to drive executive decision-making.

• Balancing System Design and User Experience: Integrating deep system-level design considerations with user-centric principles to create coherent and performant AI tools.

• Rapid Technological Evolution: Staying abreast of the fast-paced advancements in AI and machine learning, and continuously adapting design strategies accordingly.

• Cross-functional Alignment: Navigating the needs and technical constraints of engineering, product management, and research teams to achieve cohesive design solutions.

Learning & Development Opportunities:

• Deep AI Expertise: Unparalleled opportunity to specialize in cutting-edge AI design, working with generative AI, LLMs, and agentic systems.

• Advanced Data Visualization: Hone skills in transforming complex datasets into actionable insights and compelling narratives for executive audiences.

• System Design Acumen: Develop a strong understanding of the technical architecture and foundational elements that power large-scale AI products.

• Mentorship & Leadership: Opportunity to mentor junior designers and contribute to design strategy for core Google technologies.

• Industry Conferences & Training: Access to internal and external learning resources, workshops, and potentially conferences focused on AI, UX, and data visualization.

📝 Enhancement Note: The primary challenges revolve around the novelty and complexity of designing for advanced AI systems and the need for sophisticated data narrative skills. Growth opportunities are heavily skewed towards deepening expertise in these specialized, high-demand areas within a leading tech company.

💡 Interview Preparation

Strategy Questions:

• AI System Design: "Imagine you need to design a system that allows developers to query and manage the knowledge base of an autonomous AI agent. How would you approach this? What are the key user needs, system components, and interaction patterns?" (Focus on system thinking, user needs for developers, and potential LLM integration).

• Data Narrative & Executive Dashboards: "You're given a dataset showing the performance of various AI models across different tasks. How would you design a dashboard and narrative to present these findings to senior executives, enabling them to make strategic decisions about AI investment?" (Focus on data visualization, storytelling, and executive communication).

• User-Centered AI Approach: "Describe a time you had to design a user experience for a technology that was still under development or had significant technical constraints. How did you ensure a user-centered approach while working with engineers?" (Focus on balancing user needs with technical feasibility, collaboration).

Company & Culture Questions:

• Google's AI Strategy: "How do you see generative AI and agentic systems evolving at Google? What role do you believe UX plays in shaping this future?" (Research Google's AI initiatives and express informed opinions).

• Collaboration in a Large Tech Company: "Describe your experience working in large, multi-disciplinary teams. How do you ensure effective communication and alignment with engineers and product managers on complex projects?" (Highlight your cross-functional collaboration skills).

• Impact Measurement: "How would you measure the success of an AI system design aimed at improving developer efficiency or executive decision-making?" (Focus on defining KPIs, data analysis, and demonstrating ROI).

Portfolio Presentation Strategy:

• AI Systems Case Study Structure: For each relevant project, clearly outline: Problem (what was the AI system challenge?), Role (your specific contributions), Process (research, ideation, system design, prototyping, user testing), Solution (key design decisions, system architecture insights, UI elements), and Impact (metrics, user feedback, business value).

• Data Visualization Emphasis: Dedicate time to showcasing your data visualization skills. Explain your choices of chart types, dashboard layouts, and how you ensured clarity and insight.

• Technical Fluency Articulation: Be ready to discuss the technical aspects of your designs, including potential LLM integrations, data flows, and any prototyping done with code.

• Storytelling: Weave a narrative throughout your presentation that connects your passion for user experience with your ability to tackle complex AI challenges and drive measurable outcomes.

📝 Enhancement Note: Interview preparation should focus on demonstrating a unique combination of design craft, AI technical understanding, and strategic thinking. Candidates need to prove they can not only design beautiful interfaces but also conceptualize and articulate the underlying intelligence and systems that power them, all while aligning with Google's data-driven and user-centric ethos.