UX Engineer, Platform and Service Experience, Google Cloud

📍 Job Overview

Job Title: UX Engineer, Platform and Service Experience, Google Cloud

Company: Google

Location: Sunnyvale, CA, United States

Job Type: Full-Time

Category: UX Engineering / Front-End Development / GTM Operations Enablement

Date Posted: September 11, 2026

Experience Level: 5-10 Years (Mid to Senior Level)

Remote Status: On-site

🚀 Role Summary

• Drive innovation in Google Cloud's Platform and Service Experience (PSX) by bridging ambitious design goals with AI-first capabilities and production-grade engineering.

• Develop and author bespoke agentic skills, tool definitions, and orchestration flows to empower AI agents in solving complex cloud infrastructure problems.

• Design and implement interactive prototypes and server-driven UI (SDUI) patterns, leveraging modern front-end architectures and Google's Cloud Design System (CDS).

• Collaborate closely with UX Researchers and Data Scientists to establish evaluation frameworks, benchmark AI/agentic system behavior, and iterate on UX quality for enterprise users.

• Act as a technical catalyst, moving fluidly between authoring intelligent agents, prototyping novel UI patterns, and landing production-grade code within the Google3 environment.

📝 Enhancement Note: While this role is titled "UX Engineer," its responsibilities heavily align with GTM Operations Enablement and GTM Technology roles. The focus on authoring agentic skills, orchestrating LLM chains, integrating AI APIs, and building tools to accelerate UX teams indicates a strong need for individuals who can operationalize AI capabilities for internal users (developers, support, sales engineers) and enhance their productivity and effectiveness within the Google Cloud ecosystem. This implies a deep understanding of how to translate complex technical offerings into usable, efficient, and scalable solutions, which is a core tenet of GTM operations.

📈 Primary Responsibilities

• Author bespoke skills, tool definitions, and orchestration flows that enable AI agents to safely solve complex cloud infrastructure problems for internal users.

• Design and prototype benchmarking harnesses in collaboration with UX Researchers to rigorously evaluate agent accuracy, usability, and ergonomic fit for enterprise users.

• Co-architect schema-driven UI primitives to establish dynamic visual building blocks that autonomous backend agents use for effective user communication.

• Build high-fidelity, spec-driven prototypes utilizing standard Google3 architectures and Cloud Design System (CDS) components to ensure consistency and quality.

• Drive technical alignment and establish UX and Engineering standards across the Cloud Foundations community, fostering collaboration between UX, Research, Product, and Software Engineering teams.

• Integrate AI APIs and LLM chains into production or prototype systems to enhance platform capabilities and user workflows.

• Develop and maintain interactive prototypes that define innovative product experiences and accelerate the work of UX teams.

• Bridge the gap between design and engineering to ensure efficient, high-quality execution of user-facing technologies and AI-driven features.

• Partner with UX Researchers and Data Scientists to benchmark and iterate on the UX quality of AI/agentic system behavior.

• Ensure enterprise accessibility standards, optimal typography, responsive layout systems, micro-interactions, and motion design are incorporated into all developed experiences.

📝 Enhancement Note: The emphasis on "authoring agentic skills," "orchestrating LLM chains," and "integrating AI APIs" points to a need for understanding how to operationalize AI for specific business functions within Google Cloud. This extends beyond traditional UX engineering to include aspects of GTM enablement, where AI tools and agents can directly impact sales productivity, technical support efficiency, and customer success operations by providing intelligent assistance and automating complex tasks related to cloud infrastructure.

🎓 Skills & Qualifications

Education: Bachelor's degree or equivalent practical experience.

Experience:

• Minimum 4 years of experience in front-end development, technical UX design, or prototyping.

• Preferred: 5 years developing responsive, adaptive, and performant websites and applications.

• Preferred: 5 years of experience as a front-end developer, UX Engineer, creative or design technologist, or in a prototyping design environment. Required Skills:

• Proficiency in front-end development using TypeScript, JavaScript, and Angular.

• Experience in application prototyping.

• Experience in application development in one or more platform/area (e.g., web, iOS, Android, CompDes, XR).

• Strong understanding of modern front-end architectures and component-based web frameworks (e.g., Angular, Lit/Web Components, or React).

• Experience authoring agentic skills, orchestrating LLM chains, and integrating AI APIs into production or prototype systems.

• Ability to design and prototype benchmarking harnesses with UX Researchers.

• Experience co-architecting schema-driven UI primitives.

• Ability to build high-fidelity, spec-driven prototypes on standard Google3 architectures and Cloud Design System (CDS) components.

• Experience driving technical alignment across UX, Research, Product, and Software Engineering. Preferred Skills:

• Experience developing responsive, adaptive, and performant websites and applications.

• Experience with enterprise accessibility standards, typography, responsive layout systems, micro-interactions, and motion design.

• Experience partnering with UX Researchers and Data Scientists to evaluate, benchmark, and iterate on AI/agentic system behavior and UX quality.

• Familiarity with Cloud Foundations community and establishing UX/Engineering standards.

• Experience with Server-Driven UI (SDUI) patterns.

• Knowledge of Google Cloud products and services.

📝 Enhancement Note: The "Preferred Qualifications" highlight a strong leaning towards AI and agentic systems. This suggests that candidates with experience in operationalizing AI tools, building AI-powered workflows, or developing AI-driven user experiences will be highly competitive. This is directly relevant to GTM operations, where AI can automate lead qualification, personalize customer interactions, and provide real-time insights to sales and support teams.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable examples of complex front-end applications or interactive prototypes built with TypeScript, JavaScript, and Angular.

• Case studies showcasing experience in technical UX design, prototyping, and bridging design and engineering.

• Projects illustrating experience with AI APIs, LLM chains, or agentic systems, detailing the problem solved and the outcome.

• Examples of contributions to design systems or component-based frameworks, highlighting adherence to standards and best practices.

• Proof of work on projects that required deep understanding of cloud infrastructure or enterprise software. Process Documentation:

• Showcase a structured approach to designing and prototyping user experiences, including user research integration and iteration cycles.

• Document the process of developing and implementing agentic skills or AI-driven features, from concept to execution.

• Provide examples of how you have established evaluation frameworks or benchmarking processes for UX quality, particularly for AI systems.

• Detail experience in collaborating with cross-functional teams (UX, Research, Product, Engineering) to drive technical alignment and define standards.

• Demonstrate the ability to document and communicate technical designs, UI primitives, and architectural decisions effectively.

📝 Enhancement Note: A strong portfolio for this role will go beyond visual design and basic front-end code. It should highlight the candidate's ability to operationalize complex technologies like AI and LLMs into functional tools and experiences. This includes demonstrating their process for building, testing, and refining these systems, especially in the context of enterprise users and cloud infrastructure, which aligns with the operational demands of GTM technology and enablement.

💵 Compensation & Benefits

Salary Range: $132,000 - $189,000 USD per year.

Benefits:

• Target annual bonus of 15%.

• Equity grants, reflecting the company's commitment to employee ownership and long-term success.

• Comprehensive health insurance coverage.

• Access to a wide range of other benefits offered by Google, which typically include retirement savings plans (401k), paid time off, parental leave, wellness programs, employee assistance programs, and professional development opportunities.

Working Hours: Standard full-time hours, typically 40 hours per week. While the role is on-site, Google often offers flexibility within a standard workday to accommodate individual productivity peaks and personal needs, provided business requirements and team collaboration are maintained.

📝 Enhancement Note: The salary range provided is competitive for a UX Engineer role with this level of experience and specialization in a high-cost-of-living area like Sunnyvale, CA. The bonus and equity components are significant additions that increase total compensation, aligning with industry standards for large tech companies. The mention of "benefits at Google" in the source material implies a very robust and comprehensive benefits package, typical for major technology firms.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Cloud Computing, Artificial Intelligence, Software Development). Google operates at the forefront of technological innovation, with Google Cloud being a major player in the enterprise cloud market, competing with AWS and Azure. The company's culture is deeply rooted in data-driven decision-making, user-centric design, and rapid iteration.

Company Size: Google is a massive global organization, employing hundreds of thousands of individuals. This scale means access to vast resources, cutting-edge technology, and opportunities for diverse career paths, but also requires strong individual initiative and ability to navigate large structures.

Founded: Google was founded in 1998. Its history is marked by continuous innovation, from search to AI, cloud computing, and beyond. This legacy fosters a culture that values long-term vision, ambitious goals, and pushing the boundaries of what's possible.

Team Structure:

• The Platform and Service Experience (PSX) UX Engineering team is a multi-disciplinary unit operating within Google Cloud Foundations.

• This team works at the intersection of Design, Research, and Engineering, indicating a highly collaborative and integrated structure.

• Reporting lines likely involve management within the UX or Engineering leadership structure, with close collaboration with Product Management and Software Engineering teams.

• Cross-functional collaboration is a cornerstone, with UX Engineers acting as technical catalysts and driving alignment across various disciplines. Methodology:

• Data Analysis & Insights: Heavy reliance on user insights, data science, and benchmarking to inform design and development decisions, especially for AI/agentic systems.

• Workflow Planning & Optimization: Focus on creating efficient workflows for both internal users (developers, engineers) and the UX teams themselves, leveraging AI and automation.

• Automation & Efficiency: A core objective is to build tools and capabilities that accelerate UX teams and improve the efficiency of cloud infrastructure management through AI agents.

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

📝 Enhancement Note: The emphasis on "Cloud Foundations" and "Platform and Service Experience" suggests this team is critical for the core infrastructure and user experience of Google Cloud. This means the work done by this UX Engineer will have a significant impact on how developers and enterprises interact with and utilize Google Cloud services, making it a high-visibility and high-impact role within the company.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned at a Mid to Senior level (5-10 years experience). It requires not only strong technical execution but also the ability to influence technical direction, establish standards, and collaborate effectively across multiple disciplines. The expectation is for an individual contributor who can operate with significant autonomy and technical leadership within their domain.

Reporting Structure: The UX Engineer will report into a management structure within the Google Cloud UX or Engineering organization. They will work closely with Product Managers, UX Researchers, Data Scientists, and Software Engineers, forming project-specific teams.

Operations Impact: The impact of this role is substantial, directly influencing the usability, efficiency, and adoption of Google Cloud services. By developing AI-powered tools, agentic skills, and intuitive user interfaces, this role contributes to:

• Developer Productivity: Enabling developers to manage complex cloud infrastructure more easily and efficiently.

• Enterprise Adoption: Making Google Cloud more accessible and user-friendly for large organizations.

• Innovation Acceleration: Providing tools and prototypes that help Google Cloud and its users innovate faster.

• AI/ML Integration: Driving the effective and safe integration of AI capabilities into enterprise workflows.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI agent development, LLM integration, server-driven UI, and advanced front-end architectures.

• Cross-functional Leadership: Grow into roles that lead technical strategy for specific platform areas or user experiences within Google Cloud.

• Mentorship: Guide more junior engineers and contribute to the development of best practices and standards across the broader UX and engineering community.

• Product Impact: Contribute to the launch of significant new features and capabilities that define the future of Google Cloud.

• Internal Tooling & Framework Development: Lead initiatives to build foundational tools and frameworks that empower other engineering and UX teams.

📝 Enhancement Note: The role offers a unique opportunity to be at the cutting edge of AI integration within enterprise cloud platforms. For an operations-minded individual, this means understanding how to operationalize advanced AI capabilities to drive business value, improve user workflows, and enhance overall efficiency within a major cloud provider. This experience is highly transferable and valuable in the broader GTM operations and technology landscape.

🌐 Work Environment

Office Type: This is an on-site role at Google's Sunnyvale, California campus. Google campuses are known for their state-of-the-art facilities, collaborative workspaces, and extensive amenities designed to foster innovation and employee well-being.

Office Location(s): Sunnyvale, CA, United States. This location is part of Google's extensive Silicon Valley presence, offering proximity to a vibrant tech ecosystem.

Workspace Context:

• Collaborative Environment: The workspace is designed to encourage interaction, with open areas, meeting rooms, and project spaces. UX Engineers will work closely with designers, researchers, product managers, and software engineers.

• Tools and Technology: Access to Google's internal development tools, infrastructure (including Google3), cloud platforms, and cutting-edge hardware. This includes powerful workstations and development environments.

• Team Interaction: Regular team meetings, design reviews, code reviews, and cross-functional syncs are integral to the workflow, promoting continuous feedback and knowledge sharing.

Work Schedule: While the role is on-site and full-time, Google typically supports a degree of flexibility within the standard work week to accommodate individual productivity patterns and personal needs, as long as core responsibilities and team collaboration are met. This flexibility is crucial for deep work sessions required for complex technical development and prototyping.

📝 Enhancement Note: The on-site requirement in Sunnyvale emphasizes the importance of in-person collaboration, particularly for highly technical roles involving complex system design and cross-functional alignment. This environment is conducive to rapid prototyping, immediate feedback loops, and the spontaneous problem-solving that often occurs when diverse teams are co-located.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will review your application and resume, focusing on alignment with minimum and preferred qualifications, particularly experience with TypeScript, JavaScript, Angular, prototyping, and AI/agentic systems.

• Technical Phone Screen: An interview with a UX Engineer or Engineering Manager to assess technical depth in front-end development, prototyping, and understanding of AI concepts. May involve coding exercises or system design questions.

• On-site/Virtual Interviews (Loop): A series of interviews (typically 4-5) with various team members including UX Engineers, UX Researchers, Product Managers, and Engineering Managers.

  • Prototyping/Technical Design: Focus on building and discussing prototypes, demonstrating your ability to translate complex requirements into tangible user experiences. Be prepared to discuss your process, technical choices, and trade-offs.
  • AI/Agentic Systems: Questions will probe your understanding of LLM chains, agent orchestration, and AI API integration. Be ready to discuss how you approach building and evaluating these systems for enterprise use cases.
  • Collaboration & Problem-Solving: Assess your ability to work effectively with cross-functional teams, drive technical alignment, and solve complex problems. Behavioral questions will be common.
  • System Design: Questions may involve designing components of the platform, evaluating AI system behavior, or architecting server-driven UI patterns.

• Hiring Committee Review: Your interview feedback is compiled and reviewed by a hiring committee for a final decision.

Portfolio Review Tips:

• Curate Strategically: Select 3-5 of your strongest projects that best showcase your experience with TypeScript, JavaScript, Angular, prototyping, and especially AI/agentic systems or complex technical UX design.

• Focus on Impact: For each project, clearly articulate the problem you solved, your role, the technologies used, your process, and the quantifiable impact or outcome (e.g., improved efficiency, user satisfaction, adoption rates, technical feasibility).

• Showcase AI/Agentic Work: If possible, include projects where you've worked with AI APIs, LLM chains, or designed agentic skills. Detail the architecture, the challenges of integrating AI, and how you evaluated its performance and UX.

• Technical Depth: Be prepared to discuss the technical architecture, code quality, design patterns, and trade-offs made in your projects. For prototypes, explain the tools and methods used.

• Process Clarity: Demonstrate a clear, iterative design and development process. Explain how you incorporate user feedback, collaborate with stakeholders, and ensure technical feasibility and scalability.

• Google Cloud Relevance: If you have experience with cloud infrastructure, enterprise software, or building tools for developers, highlight these aspects.

Challenge Preparation:

• Technical Exercises: Expect coding challenges focused on front-end development (TypeScript/JavaScript, Angular) and potentially system design related to UI components or agent interactions.

• Design/Prototyping Challenges: You might be asked to quickly sketch or prototype a solution for a given problem, demonstrating your problem-solving and rapid ideation skills.

• AI/Agentic System Design: Be prepared to discuss how you would design a system for a specific cloud infrastructure problem using AI agents, including how you'd define skills, orchestrate flows, and evaluate success.

• Behavioral Scenarios: Prepare examples using the STAR method (Situation, Task, Action, Result) to illustrate your experience with collaboration, problem-solving, technical leadership, and handling ambiguity.

📝 Enhancement Note: The interview process at Google is rigorous and focuses on assessing technical depth, problem-solving skills, and cultural fit. For this specific role, demonstrating a strong understanding of how to operationalize AI and build efficient, scalable technical solutions for enterprise users will be paramount. Your portfolio should serve as concrete evidence of these capabilities.

🛠 Tools & Technology Stack

Primary Tools:

• Front-end Development: TypeScript, JavaScript, Angular (primary framework), HTML5, CSS3.

• Prototyping Tools: Figma, Sketch, Adobe XD, or custom in-house tools for rapid prototyping and UI design.

• Development Environment: Google3 (Google's monorepo build system), internal Google development tools.

• AI/ML Tools: Experience with Python (often used for backend AI logic), familiarity with AI APIs (e.g., Google Cloud AI Platform services, Vertex AI), LLM frameworks, and agent orchestration tools.

Analytics & Reporting:

• Data Analysis: Internal Google analytics tools, potentially BigQuery for data exploration.

• Benchmarking Tools: Custom-built harnesses for evaluating AI agent performance and UX quality.

• Dashboarding: Tools for visualizing performance metrics and user feedback (specific tools may be internal).

CRM & Automation:

• Internal Systems: While not explicitly stated, expect to interact with internal Google Cloud platforms and potentially CRM-like systems for understanding user needs and product roadmaps.

• Automation: Focus on building automation through agentic skills and server-driven UI to streamline complex cloud infrastructure tasks.

📝 Enhancement Note: Proficiency in TypeScript, JavaScript, and Angular is non-negotiable. Beyond that, a strong understanding of AI/ML concepts, experience with AI APIs, and the ability to work within a large, integrated development environment like Google3 are critical. The emphasis on building "bespoke skills" and "orchestration flows" for AI agents suggests familiarity with logical programming and workflow design is also highly beneficial.

👥 Team Culture & Values

Operations Values:

• User Focus: "Focus on the user and all else will follow" is a core Google principle. This role translates that by ensuring AI agents and platform experiences are intuitive, efficient, and valuable for developers and enterprise users.

• Data-Driven: Decisions are made based on rigorous data analysis, user research, and performance benchmarking, especially for AI system behavior and UX quality.

• Innovation & Ambition: Encouragement to push boundaries, explore novel solutions (like agentic AI), and tackle complex, ambitious problems in cloud infrastructure.

• Collaboration & Alignment: Strong emphasis on working across disciplines (UX, Research, Product, Engineering) to achieve shared goals and establish technical standards.

• Efficiency & Scalability: Building tools and experiences that not only solve immediate problems but are also scalable and efficient for a large enterprise user base.

Collaboration Style:

• Cross-Functional Integration: UX Engineers are embedded within multi-disciplinary teams, acting as a bridge between design vision and engineering reality.

• Process Review & Feedback: A culture of continuous feedback through design reviews, code reviews, and collaborative problem-solving sessions.

• Knowledge Sharing: Active participation in sharing best practices, new techniques, and insights across teams, particularly regarding AI integration and UX standards.

📝 Enhancement Note: The culture at Google, and within this team, values individuals who are not only technically skilled but also proactive, collaborative, and passionate about solving complex problems that impact millions of users. For this role, demonstrating an understanding of how to operationalize AI for enterprise efficiency and user enablement will resonate strongly.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of Cloud Infrastructure: Navigating and simplifying the intricate nature of cloud computing for AI-driven management.

• AI Safety and Reliability: Ensuring AI agents operate safely, accurately, and reliably when managing critical cloud infrastructure.

• Bridging Design and Production: Translating ambitious, experimental designs and AI concepts into robust, production-grade code and experiences.

• Cross-Disciplinary Alignment: Effectively communicating technical concepts and driving consensus among diverse teams (UX, Research, Product, Engineering).

• Rapid Technological Evolution: Staying current with the fast-paced advancements in AI, LLMs, and front-end technologies.

Learning & Development Opportunities:

• AI & Agentic Systems Expertise: Deepen knowledge in advanced AI development, prompt engineering, LLM fine-tuning, and agent orchestration.

• Platform & Cloud Technologies: Gain in-depth understanding of Google Cloud's architecture, services, and enterprise use cases.

• Advanced Front-End Architecture: Master cutting-edge front-end frameworks, component-based design, and server-driven UI patterns.

• Leadership & Mentorship: Opportunities to lead technical initiatives, mentor junior team members, and contribute to setting industry standards.

• Industry Conferences & Training: Access to Google's extensive learning resources, internal training programs, and potential for attending industry conferences.

📝 Enhancement Note: The challenges presented are significant but also offer immense learning potential. For an operations-minded professional, these challenges represent opportunities to build expertise in operationalizing cutting-edge AI for enterprise solutions, a highly sought-after skill set in today's market.

💡 Interview Preparation

Strategy Questions:

• AI Agent Design: "Describe how you would design an AI agent to help a user troubleshoot common network connectivity issues within Google Cloud. What skills would it need? How would you orchestrate its actions and ensure it provides accurate guidance?"

  • Preparation: Think about breaking down the problem into discrete steps, defining user intents, designing conversational flows, and considering error handling and escalation paths.

• Collaboration Scenarios: "You've prototyped a new UI pattern for managing cloud resources, but the engineering team has concerns about its feasibility and performance. How would you approach resolving this disagreement and ensuring the best outcome for the user and the product?"

  • Preparation: Focus on active listening, data-driven arguments, finding common ground, and proposing iterative solutions. Highlight your ability to balance user needs with technical constraints.

• Process Optimization: "Imagine you need to build a tool to help UX researchers benchmark the performance of AI agents. What would be your approach to designing and prototyping this benchmarking harness, and what metrics would you prioritize?"

  • Preparation: Discuss your process for requirements gathering, iterative design, defining key performance indicators (KPIs), and prototyping the user interface for the harness.

Company & Culture Questions:

• User Focus: "How do you ensure your technical designs and prototypes truly serve the end-user, especially when working with complex technologies like AI?"

  • Preparation: Reference Google's "Focus on the user" principle and provide examples of how you've incorporated user feedback or research into your work.

• Team Dynamics: "Describe a time you worked on a project with significant ambiguity or evolving requirements. How did you navigate that situation and contribute to the team's success?"

  • Preparation: Showcase adaptability, proactivity, and effective communication skills within a team setting.

• Impact Measurement: "How would you measure the success of an AI agent designed to assist developers with cloud infrastructure tasks? What metrics would you track, and why?"

  • Preparation: Think about metrics beyond just task completion, such as time saved, error reduction, user satisfaction, and adoption rates.

Portfolio Presentation Strategy:

• Tell a Story: For each project, craft a narrative that clearly outlines the problem, your role and approach, the technical challenges overcome, your specific contributions (especially regarding AI/agentic aspects), and the impact achieved.

• Highlight AI/Agentic Work: Dedicate specific slides or talking points to projects involving AI APIs, LLM chains, or agent development. Explain the architecture, the integration process, and how you evaluated performance and user experience.

• Showcase Technical Prowess: Be prepared to walk through code snippets (if applicable and appropriate), discuss design patterns, and explain your technical decisions. For prototypes, demonstrate their interactivity and key features.

• Quantify Impact: Whenever possible, use data and metrics to demonstrate the value and success of your projects.

• Engage and Discuss: Treat the portfolio review as a conversation. Be open to questions, delve deeper into specific aspects, and tailor your presentation to the interviewer's interests.

📝 Enhancement Note: The interview process at Google is designed to assess not only technical skills but also problem-solving abilities, collaboration aptitude, and alignment with Google's core values. For this UX Engineer role, demonstrating a strong grasp of AI, its practical application in enterprise contexts, and the ability to translate complex technical requirements into user-friendly experiences will be key to success.

📌 Application Steps

To apply for this UX Engineer position at Google:

• Submit your application through the official Google Careers portal link provided in the job listing.

• Portfolio Customization: Tailor your resume and portfolio to highlight your experience with TypeScript, JavaScript, Angular, prototyping, and specifically any work involving AI APIs, LLM chains, or agentic systems. Quantify your achievements with relevant metrics.

• Resume Optimization: Ensure your resume clearly articulates your experience in front-end development, technical UX design, and any contributions to platform or service experiences. Use keywords from the job description naturally.

• Interview Preparation: Thoroughly prepare for technical and behavioral interviews. Practice articulating your thought process for problem-solving, system design, and AI integration. Prepare specific examples using the STAR method.

• Company Research: Understand Google's mission, Google Cloud's offerings, and the specific team's focus on Platform and Service Experience. Research their approach to AI and user-centric design to demonstrate genuine interest and cultural alignment.

⚠️ 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.