Director, UX, Search Model Behavior

📍 Job Overview

Job Title: Director, UX, Search Model Behavior

Company: Google

Location: San Francisco, CA / Mountain View, CA

Job Type: Full-time

Category: User Experience (UX) / Artificial Intelligence (AI) Operations

Date Posted: July 20, 2026

Experience Level: Senior Level (10+ years)

Remote Status: On-site

🚀 Role Summary

• Lead the strategic vision and execution of User Experience for Search Model Behavior, focusing on AI-driven conversational agents and generative features like AI Overviews and AI Mode.

• Bridge the critical gap between human-centered design principles and machine learning engineering to define fundamental interaction paradigms for Google's evolving Search product.

• Manage and scale a multidisciplinary team of UX Designers, UX Researchers, Model Behavior Specialists, and Program Managers, fostering a collaborative environment at the intersection of design, linguistics, and AI.

• Develop and own overarching voice, tone, and behavioral policies for Search AI, establishing constitutional guidelines and conversational frameworks to ensure helpful, safe, and user-aligned AI responses.

📝 Enhancement Note: This role is distinctly positioned within "AI Operations" from an operational perspective, focusing on the systematic definition, implementation, and refinement of AI model behavior through UX and technical frameworks. While the title is "Director, UX", the core responsibilities and required skills heavily lean into managing the operational aspects of AI model interaction design and evaluation at scale.

📈 Primary Responsibilities

• Define and execute the overarching Search Model Behavior strategy, establishing foundational frameworks for model persona, conversational architecture, and core interaction principles.

• Translate qualitative user needs and research insights into quantitative technical signals and actionable protocols for model training, system prompting, Supervised Fine-Tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF).

• Develop and implement rigorous qualitative rubrics and human-evaluation frameworks to continuously measure model output quality, mitigate hallucinations, and reduce bias across all AI surfaces.

• Own and scale the voice, tone, and behavioral policies of Search AI, creating constitutional guidelines and conversational frameworks that govern model responses to complex, ambiguous, or sensitive queries.

• Partner directly with Machine Learning and Engineering leadership to embed UX principles throughout the model development lifecycle, influencing technical architecture and driving best practices for AI interaction design.

📝 Enhancement Note: The responsibilities emphasize a strategic operational role in AI development, focusing on governance, process definition, and cross-functional team leadership. The emphasis on "scaling" policies and frameworks, and "embedding UX principles into the model development lifecycle," highlights a strong operational component beyond traditional UX design.

🎓 Skills & Qualifications

Education: Bachelor’s degree in English, Journalism, Media, Communications, HCI, or a related field; or equivalent practical experience.

Experience: 15 years of experience managing multi-disciplinary design and technical interaction teams, with a proven track record of scaling teams within highly matrixed environments.

Required Skills:

• Extensive experience managing multi-disciplinary design and technical interaction teams, demonstrating scaled leadership capabilities.

• Deep expertise in RLHF (Reinforcement Learning from Human Feedback), model fine-tuning techniques, and developing robust evaluation frameworks (e.g., SxS testing, human-evaluation rubrics).

• Proven ability to foster divergent design thinking and shepherd radical ideas that can evolve large-scale products.

• Strong technical aptitude to understand global product ecosystems and effectively influence cross-functional stakeholders.

• Experience in defining and executing model behavior strategies for complex AI platforms. Preferred Skills:

• Experience leading the model behavior vision and execution for large-scale products with a strong focus on localization, usability, and accessibility best practices for global AI platforms.

• Exceptional communication and scaled leadership skills, adept at navigating and defining methods in ambiguous AI spaces.

• Curiosity and ability to understand the global product ecosystem and drive scalable model design solutions.

• Experience building and scaling multidisciplinary teams within highly matrixed environments.

• Familiarity with defining constitutional guidelines and conversational frameworks for AI.

📝 Enhancement Note: The "15 years of experience managing multi-disciplinary design and technical interaction teams" is a significant requirement, indicating a senior leadership role focused on operationalizing UX and AI behavior across large, complex organizations. The emphasis on team management, scaling, and navigating ambiguity points to a strong operational leadership component.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a portfolio showcasing successful leadership in defining and implementing user interaction paradigms for complex technical products, particularly in AI or ML-driven systems.

• Include case studies that highlight the process of translating user needs and research into actionable technical requirements for model development (e.g., SFT, RLHF protocols).

• Showcase examples of developing and scaling evaluation frameworks, rubrics, or testing methodologies to measure and improve AI model output quality, safety, and fairness.

• Present evidence of defining and implementing voice, tone, and behavioral policies for AI systems, with clear articulation of the process and impact. Process Documentation:

• Detail the methodology used for embedding UX principles into the ML development lifecycle, including collaboration with engineering and research teams.

• Illustrate the process of creating and implementing constitutional guidelines and conversational frameworks for AI models, ensuring alignment with user intent and safety standards.

• Provide examples of how qualitative user research and quantitative data were integrated to inform model behavior and interaction design decisions.

• Describe the approach to scaling leadership and managing multidisciplinary teams to achieve strategic objectives in complex, ambiguous environments.

📝 Enhancement Note: While a traditional UX portfolio is expected, the emphasis here should be on demonstrating operational proficiency in managing AI model behavior. This includes process documentation for how UX principles are integrated into ML lifecycles, how evaluation frameworks are built and scaled, and how policies are defined and implemented for AI systems.

💵 Compensation & Benefits

Salary Range: $275,000 - $383,000 (USD) per year.

Benefits:

• 30% bonus target, providing performance-based financial incentives.

• Equity grants, offering ownership and long-term financial participation in Google's success.

• Comprehensive benefits package, typically including health insurance (medical, dental, vision), retirement savings plans (e.g., 401k), paid time off, parental leave, and wellness programs.

• Access to Google's extensive employee development resources, including training, workshops, and learning platforms.

Working Hours: Standard full-time (40 hours per week) with flexibility expected for a leadership role in a dynamic, global technology environment.

📝 Enhancement Note: The salary range provided is a strong indicator of the senior leadership and strategic impact expected from this role. The bonus target and equity are standard for such senior positions at Google, reflecting performance-driven compensation. The working hours, while stated as standard, will likely require significant dedication due to the nature of leading complex AI initiatives.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Internet Services, AI, Search Engines)

Company Size: Large Enterprise (10,000+ employees)

Founded: 1998 by Larry Page and Sergey Brin, Google has grown into a global technology leader, renowned for innovation in search, AI, cloud computing, and more. The company's culture emphasizes innovation, data-driven decision-making, and a focus on user impact.

Team Structure:

• This role leads a multidisciplinary team comprising UX Designers, UX Researchers, Model Behavior Specialists, and Program Managers, operating within the broader Google Search organization.

• The reporting structure is likely to be within a senior UX or Product leadership hierarchy, with direct collaboration with ML/Engineering leadership.

• Cross-functional collaboration is paramount, involving close partnerships with AI/ML engineers, product managers, researchers, policy experts, and legal teams to define and implement AI model behavior. Methodology:

• Data analysis and insights are central, leveraging user research, behavioral data, and model performance metrics to inform strategic decisions.

• Workflow planning and optimization strategies are critical for managing the complex development lifecycle of AI features, ensuring efficiency and scalability.

• Automation and efficiency practices are encouraged, particularly in evaluating and refining AI model outputs and interaction patterns.

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

📝 Enhancement Note: Google's culture is known for its emphasis on innovation, speed, and impact. For an operations role in AI, this translates to a fast-paced environment where rigorous, data-driven processes are essential for managing the complexities of AI development and deployment at scale. The "highly matrixed environment" mentioned in qualifications is a key aspect of Google's operational structure.

📈 Career & Growth Analysis

Operations Career Level: Director level, signifying significant leadership responsibility for a critical functional area (AI Model Behavior UX). This role requires strategic oversight, team management, and substantial influence on product direction and technical architecture.

Reporting Structure: The Director will likely report to a VP or Senior Director within the Google Search organization, overseeing a dedicated team of specialists. They will also have strong, direct reporting lines to ML/Engineering leadership for collaborative development.

Operations Impact: This role has a direct and profound impact on how billions of users interact with Google's core Search product. By shaping the behavior and conversational capabilities of AI features, the Director influences user trust, engagement, and the overall utility of information discovery, directly contributing to Google's mission.

Growth Opportunities:

• Strategic Leadership Expansion: Potential to expand leadership scope to encompass broader AI UX strategies, or transition into senior product leadership roles within AI or core Search.

• Specialization Deepening: Opportunity to become a recognized authority in AI model behavior, UX for generative AI, and advanced evaluation methodologies within the industry.

• Cross-Functional Leadership: Develop expertise in navigating and leading large-scale, cross-functional initiatives across Google's vast product ecosystem, honing skills for future executive roles.

📝 Enhancement Note: This role represents a significant step in an operations-focused career within AI product development. The growth path emphasizes leadership, strategic influence, and deep specialization in a rapidly evolving field, offering substantial potential for career advancement within Google or the broader tech industry.

🌐 Work Environment

Office Type: Google operates modern, state-of-the-art office facilities designed to foster collaboration, innovation, and employee well-being. These environments typically include open workspaces, dedicated team areas, meeting rooms, and amenities.

Office Location(s): The role is based in either San Francisco, CA, or Mountain View, CA, offering access to major tech hubs and vibrant urban environments. These locations provide excellent connectivity and a dynamic professional ecosystem.

Workspace Context:

• The workspace will be highly collaborative, requiring frequent interaction with diverse teams, including UX researchers, designers, ML engineers, and product managers.

• Access to cutting-edge tools, research methodologies, and internal Google platforms for AI development, testing, and UX evaluation will be provided.

• Opportunities for informal and formal knowledge sharing, brainstorming sessions, and cross-pollination of ideas with other leading AI and UX professionals within Google.

Work Schedule: While a standard 40-hour work week is the baseline, leadership roles at Google, especially in fast-moving fields like AI, often require flexibility and dedication beyond typical hours to meet project deadlines and strategic objectives.

📝 Enhancement Note: The on-site work requirement emphasizes Google's commitment to fostering a collaborative and innovative in-person culture, crucial for the complex, cross-functional work involved in AI development.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will assess your resume and qualifications against the minimum and preferred requirements, potentially followed by a brief introductory call.

• Hiring Manager Interview: A deep dive into your experience, leadership style, and strategic thinking, focusing on your ability to manage multidisciplinary teams and navigate AI development complexities. Be prepared to discuss your approach to defining model behavior and evaluation frameworks.

• Team/Peer Interviews: Interviews with potential team members (UX designers, researchers, model behavior specialists) and cross-functional peers (ML engineers, PMs). These will assess your collaboration style, technical aptitude, and ability to translate UX needs into engineering realities. Expect scenario-based questions.

• Leadership/Executive Interview: A final interview with senior leadership to evaluate your strategic vision, cultural fit, and potential impact on Google's AI product roadmap. This may include a presentation or discussion of a hypothetical challenge.

Portfolio Review Tips:

• Focus on Operational Impact: Showcase how you've operationalized UX principles within ML/AI development lifecycles. Highlight processes you've implemented for scaling, evaluation, and policy definition.

• Quantify Results: For each case study, clearly articulate the impact of your work using relevant metrics (e.g., improvements in user satisfaction, reduction in model errors, successful scaling of features).

• Demonstrate Leadership: Present examples of how you've led and grown multidisciplinary teams, fostered collaboration, and influenced technical direction.

• Address Ambiguity: Be prepared to discuss how you approach undefined spaces and develop strategic frameworks for novel problems, especially concerning AI behavior.

Challenge Preparation:

• Expect potential case studies or hypothetical scenarios related to defining the behavior of a new AI feature, addressing bias, or improving conversational flow.

• Prepare to articulate your thought process for translating user needs into specific technical requirements for ML teams.

• Practice presenting complex ideas clearly and concisely, demonstrating your ability to influence technical and design stakeholders.

📝 Enhancement Note: The interview process is designed to assess not just UX expertise but also operational leadership, strategic thinking, and the ability to execute complex AI initiatives within a large, technical organization. A strong portfolio demonstrating process and impact is critical.

🛠 Tools & Technology Stack

Primary Tools:

• UX Design & Prototyping: Proficiency with industry-standard tools such as Figma, Sketch, Adobe Creative Suite, and potentially advanced prototyping tools for interactive AI experiences.

• User Research Platforms: Experience with tools for surveys, usability testing, qualitative data analysis (e.g., Dovetail, UserTesting.com, Qualtrics).

• Project Management & Collaboration: Familiarity with tools like Jira, Asana, or Google Workspace for team coordination, task tracking, and documentation.

Analytics & Reporting:

• Data Analysis Tools: Experience with SQL, Python (for data analysis), or Google's internal data analysis tools to interpret user behavior and model performance metrics.

• Business Intelligence Platforms: Familiarity with tools like Tableau, Looker Studio (formerly Google Data Studio), or similar for creating dashboards and visualizing key performance indicators (KPIs) related to model behavior and user engagement.

CRM & Automation:

• While not a direct CRM role, understanding of how user data is managed and how feedback loops inform model training is crucial. Experience with systems that facilitate feedback collection and integration into ML pipelines is beneficial.

• Familiarity with AI/ML platforms and frameworks (e.g., TensorFlow, PyTorch, Google AI Platform) for understanding the technical underpinnings of model development and fine-tuning.

📝 Enhancement Note: Proficiency in standard UX tools is assumed. The key differentiator will be understanding how these tools integrate with ML development lifecycles and how data from these tools informs AI model behavior and evaluation. Familiarity with AI-specific platforms and data analysis for ML is highly advantageous.

👥 Team Culture & Values

Operations Values:

• User Focus: A deep commitment to understanding and serving user needs, ensuring AI experiences are helpful, intuitive, and safe.

• Data-Driven Decision Making: Relying on rigorous data analysis, user research, and empirical evidence to guide strategy and measure impact.

• Innovation & Experimentation: Encouraging creative problem-solving and a willingness to explore novel approaches in AI interaction design and behavior.

• Collaboration & Inclusivity: Fostering a team environment where diverse perspectives are valued, and cross-functional partnerships thrive to achieve shared goals.

• Integrity & Responsibility: Upholding high ethical standards in AI development, focusing on mitigating bias and ensuring responsible deployment of technology.

Collaboration Style:

• Highly collaborative, working in a matrixed environment that requires strong communication and negotiation skills to align diverse stakeholders.

• Emphasis on open feedback loops, constructive critique, and shared ownership of product outcomes.

• Knowledge sharing is actively encouraged through internal presentations, documentation, and cross-team initiatives.

📝 Enhancement Note: Google's operational culture emphasizes a blend of high-performance expectations with a supportive, collaborative environment. For this role, it means being comfortable with ambiguity, driving initiatives with data, and working effectively across many specialized teams to deliver impactful AI experiences.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating AI Ambiguity: Defining clear, scalable interaction paradigms and behavioral policies for rapidly evolving generative AI technologies, where best practices are still emerging.

• Balancing Innovation with Safety: Ensuring AI features are cutting-edge and helpful while rigorously mitigating risks of bias, misinformation, and harmful outputs.

• Scaling Cross-Functional Alignment: Effectively influencing and coordinating efforts across numerous engineering, research, product, and policy teams within a large organization to achieve a unified model behavior strategy.

• Measuring Intangibles: Developing robust frameworks to evaluate subjective qualities like "conversational quality," "persona alignment," and "trustworthiness" in AI interactions.

Learning & Development Opportunities:

• AI Ethics & Safety Specialization: Deepen expertise in the ethical considerations and safety protocols for advanced AI systems.

• Advanced ML/NLP Understanding: Further develop technical comprehension of ML models, NLP, and generative AI techniques to enhance collaboration with engineering teams.

• Leadership Development Programs: Access to Google's extensive leadership training and mentorship programs to hone skills in managing large, complex teams and driving strategic initiatives.

📝 Enhancement Note: This role presents significant intellectual challenges inherent in pioneering new frontiers in AI UX. The growth opportunities are substantial, offering a chance to shape the future of AI interaction and build a career at the forefront of technological advancement.

💡 Interview Preparation

Strategy Questions:

• "Describe your approach to defining the voice, tone, and persona for a large-scale AI conversational agent. How would you ensure consistency across diverse query types and user contexts?"

  • Preparation: Think about frameworks for persona definition, content guidelines, and iterative testing. Emphasize how you'd translate user needs into concrete behavioral rules for the model.

• "How would you establish a robust evaluation framework for AI Overviews to measure helpfulness, accuracy, and safety? What metrics would you prioritize, and how would you operationalize this process with ML/Engineering?"

  • Preparation: Focus on the blend of qualitative (human evaluation, rubrics) and quantitative (SxS testing, error analysis) approaches. Detail the workflow for feedback integration into model fine-tuning.

• "Imagine you need to influence a critical technical decision in the ML pipeline that conflicts with a UX best practice. How would you approach this situation to achieve the best outcome for the user and the product?"

  • Preparation: Prepare to discuss your communication strategy, data-backed argumentation, and problem-solving skills in navigating technical constraints and UX principles. Company & Culture Questions:

• "What excites you most about Google's mission and its role in the future of AI search?"

  • Preparation: Research Google's recent AI announcements, its impact on information access, and its long-term vision. Connect your passion for UX and AI behavior to this mission.

• "How do you foster a collaborative and inclusive culture within a multidisciplinary team, especially when working on cutting-edge, ambiguous projects?"

  • Preparation: Share specific examples of how you've built trust, encouraged diverse perspectives, and managed conflicts within teams. Portfolio Presentation Strategy:

• Structure for Impact: Organize your portfolio to clearly articulate the problem, your role, the process/operations you implemented, the challenges faced, and the quantifiable results.

• Highlight Operationalization: For each project, emphasize how you translated UX strategy into actionable steps for development teams and how you managed the operational aspects of implementation and evaluation.

• Focus on AI/ML Relevance: Select projects that best demonstrate your experience with complex systems, data-driven decision-making, and ideally, AI or machine learning-adjacent work.

• Be Ready to Discuss Trade-offs: AI development often involves trade-offs. Be prepared to discuss how you balanced competing priorities (e.g., speed vs. safety, innovation vs. reliability).

📝 Enhancement Note: The interview questions are designed to probe your strategic thinking, operational leadership, and ability to navigate the complex interplay between UX and advanced AI development. Your portfolio should serve as tangible evidence of your capabilities in these areas.

📌 Application Steps

To apply for this operations position:

• Submit your application through the Google Careers portal link provided.

• Tailor your resume: Highlight your 15+ years of experience in managing multidisciplinary teams, your expertise in RLHF and evaluation frameworks, and your leadership in defining AI behavior. Use keywords from the job description naturally.

• Prepare your portfolio: Curate case studies that demonstrate your operational leadership in UX for complex technical products, especially those involving AI/ML. Focus on processes, scaling, and measurable impact.

• Research Google's AI initiatives: Understand Google's current AI strategy, its approach to generative AI, and its commitment to responsible AI development. This will inform your answers and demonstrate your genuine interest.

• Practice articulating your operational approach: Be ready to discuss how you translate UX strategy into actionable ML development protocols and how you build and manage teams to drive these complex initiatives.

⚠️ 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.

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