Senior Software Engineering Manager, Agentic and UI Automation Experiences

📍 Job Overview

Job Title: Senior Software Engineering Manager, Agentic and UI Automation Experiences

Company: Google

Location: Mountain View, California, United States

Job Type: Full-time

Category: Software Engineering Management / AI & Machine Learning

Date Posted: 2026-09-21

Experience Level: 10+ years

Remote Status: On-site

🚀 Role Summary

• Lead a team of software engineers focused on integrating agentic capabilities and AI-powered experiences into the Android operating system.

• Drive the technical vision and execution for advanced machine learning models, particularly Large Language Models (LLMs), to redefine mobile interactions.

• Oversee the development of AI agents capable of understanding user behavior, interpreting screen content, and executing actions via UI automation and tool calling.

• Foster a collaborative environment that encourages innovation in areas like NLP, computer vision, and on-device model optimization for mobile hardware.

• Contribute to product strategy and manage large-scale projects with significant cross-functional collaboration across Google.

📝 Enhancement Note: This role is deeply embedded within the AI and Machine Learning domain, with a specific focus on applying cutting-edge LLM technology to the Android platform. The "Agentic and UI Automation Experiences" aspect indicates a strong emphasis on creating intelligent systems that can interact with and manipulate user interfaces programmatically, a key area in modern AI application development. The Senior Manager title implies significant leadership scope, including team growth, strategic direction, and cross-functional alignment.

📈 Primary Responsibilities

• Lead and mentor a team of software engineers in the design, development, and deployment of sophisticated AI solutions, including LLM-based agents.

• Drive the implementation of UI automation capabilities, enabling AI agents to understand screen context and execute user-intended actions through APIs and tool calling.

• Optimize machine learning models for efficient on-device performance, ensuring low latency and high responsiveness on mobile hardware.

• Collaborate closely with cross-functional teams, including GDM, Gemma Frontier, Gemini app, and Android framework teams, to ensure seamless integration and alignment.

• Contribute to the strategic roadmap for AI integration within Android, identifying opportunities for agentic capabilities to enhance user experience and productivity.

• Manage project timelines, resources, and technical direction for large-scale, international projects.

• Foster a culture of innovation, continuous learning, and technical excellence within the engineering team.

• Conduct performance reviews, provide career development guidance, and support the growth of individual team members.

📝 Enhancement Note: The responsibilities highlight a blend of technical leadership, people management, and strategic product input. The emphasis on "agents that interpret screen content, understand user behavior, and execute actions through APIs" points to complex AI development requiring expertise in areas like computer vision, natural language understanding, and intelligent automation. Close collaboration with specialized teams like "Gemma Frontier" suggests involvement in foundational model development or adaptation.

🎓 Skills & Qualifications

Education:

• Bachelor’s degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.

• Master's degree or PhD in Machine Learning, or equivalent industry experience in applied Machine Learning/Machine Learning Research, is preferred. Experience:

• Minimum of 8 years of hands-on programming experience in languages such as C++, Java, Python, Kotlin, or Go.

• A minimum of 5 years in a technical leadership role, guiding engineering projects and technical direction.

• A minimum of 5 years in a people management or team leadership role, responsible for team growth, performance, and development.

• Experience with machine learning, large language models (LLMs), Python, and machine learning optimization techniques. Required Skills:

• Proficient programming skills in at least one of C++, Java, Python, Kotlin, or Go.

• Demonstrated experience in technical leadership and people management.

• Strong understanding of machine learning principles and practical application.

• Experience with large language models (LLMs) and their integration into software products.

• Experience in optimizing machine learning models for performance and efficiency.

• Excellent problem-solving, analytical, and critical thinking skills.

• Strong communication and interpersonal skills, with the ability to collaborate effectively across diverse teams. Preferred Skills:

• Expertise in Natural Language Processing (NLP), LLMs, and Computer Vision.

• Knowledge of the Android framework and its architecture.

• Experience with system design for large-scale, distributed systems.

• Familiarity with UI automation frameworks and methodologies.

• Experience with API integration and tool calling mechanisms for AI agents.

• Passionate and respectful team player with a proven ability to grow and develop engineering teams.

📝 Enhancement Note: The combination of extensive programming experience, deep ML/LLM expertise, and significant leadership experience (both technical and people management) signifies a senior-level role. The preferred qualifications, particularly Android framework knowledge and NLP/CV expertise, are highly relevant for developing advanced agentic capabilities on a mobile platform.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable experience in leading the development and deployment of complex software projects, ideally involving AI or ML components.

• Case studies showcasing successful team leadership, including team growth, mentorship, and performance management.

• Examples of technical strategy development and execution, particularly in areas related to ML model integration or system architecture.

• Evidence of driving process improvements or optimizations within engineering workflows.

• Documentation or presentations detailing the architecture and implementation of ML-powered features or systems. Process Documentation:

• Examples of how you've documented complex technical designs, ML model architectures, or system integrations.

• Evidence of establishing and refining engineering processes for areas such as code reviews, testing strategies, or deployment pipelines, especially for ML-intensive projects.

• Documentation illustrating workflow design and optimization for AI agent development, including aspects like data pipelines, model training, and inference.

• Records of implementing and measuring the impact of new processes or system enhancements related to AI development or UI automation.

📝 Enhancement Note: For a Senior Engineering Manager role focused on AI and automation, a portfolio should emphasize not just technical achievements but also leadership impact. This includes evidence of strategic thinking, team development, and successful project delivery. The ability to articulate complex technical concepts and demonstrate process improvement is crucial.

💵 Compensation & Benefits

Salary Range:

• US: $262,000 - $364,000 (USD) per year.

📝 Enhancement Note: This salary range is provided by Google for this specific role in the US. It is based on market data for Senior Software Engineering Manager roles at major tech companies, considering the location (Mountain View, CA, a high cost of living area) and the specialized nature of AI/ML and leadership. The range reflects a significant level of experience and responsibility.

Benefits:

• Bonus Target: 25% target bonus, providing performance-based financial incentives.

• Equity: Stock options or grants, offering long-term financial participation in Google's success.

• Health Insurance: Comprehensive health, dental, and vision insurance coverage.

• Retirement Savings Plan: Access to a 401(k) plan with potential company matching.

• Paid Time Off: Generous vacation, sick leave, and paid holidays.

• Parental Leave: Supportive policies for new parents.

• Wellness Programs: Resources and programs focused on employee well-being.

• Professional Development: Opportunities for training, conferences, and further education.

Working Hours:

• Standard full-time hours, typically 40 hours per week.

• Flexibility may be available, but the role requires significant availability for team management, project oversight, and cross-functional collaboration, especially given the on-site requirement.

📝 Enhancement Note: Google's benefits package is known for being comprehensive, aiming to support employees' financial, physical, and mental well-being, as well as their professional growth. The bonus and equity components are significant for senior roles, aligning compensation with company performance and long-term commitment.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Software Development, Artificial Intelligence, Mobile Platforms)

Company Size: Very Large (Over 10,000 employees)

Founded: 1998

Company Description: Google is a global technology leader focused on organizing the world's information and making it universally accessible and useful. It is renowned for its search engine, but also operates extensively in cloud computing, artificial intelligence, software, hardware, and online advertising.

Company Specialties: Search, Advertising, Cloud Computing, AI, Machine Learning, Software Development, Mobile Operating Systems (Android), Hardware Devices, Productivity Tools.

Team Structure:

• The role is within the Platforms and Devices team, specifically focusing on AI-powered operating systems and mobile interactions.

• The team likely comprises specialized engineers in ML, NLP, computer vision, Android development, and potentially UI/UX specialists.

• Reporting structure will be to a Director or VP of Engineering within the Platforms and Devices organization.

• Cross-functional collaboration is essential, involving teams like GDM (likely Google Developer Marketing or a similar partner group), Gemma Frontier (core model development), Gemini app team, and the central Android framework team. Methodology:

• Data-Driven Decision Making: Emphasis on metrics, A/B testing, and performance analysis to guide product development and strategy.

• Agile Development: Likely utilizes agile methodologies for iterative development, rapid prototyping, and continuous improvement.

• Research & Innovation: Strong focus on pushing the boundaries of AI and ML, with dedicated resources for research and experimentation.

• Scalability & Performance: Core tenets of Google's engineering culture, ensuring solutions can handle massive scale and deliver optimal performance.

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

📝 Enhancement Note: Google's culture is characterized by innovation, data-driven decision-making, and a focus on solving complex problems at scale. For this role, the culture emphasizes cutting-edge AI research and development, with a strong emphasis on product impact and cross-functional collaboration. The "Platforms and Devices" team is critical for Google's hardware and software ecosystem strategy.

📈 Career & Growth Analysis

Operations Career Level: Senior Software Engineering Manager. This level signifies a leadership role responsible for multiple teams, significant project scope, and strategic influence. It requires a deep blend of technical expertise, people management skills, and business acumen.

Reporting Structure: The role reports to a higher-level engineering leader (e.g., Director, VP) and manages multiple teams of software engineers, potentially including other engineering leads or managers.

Operations Impact: This role has a direct impact on the evolution of the Android operating system, shaping how billions of users interact with their devices through AI. The integration of agentic capabilities and UI automation will define the future of mobile user experience, influencing product strategy, user engagement, and competitive positioning in the AI space.

Growth Opportunities:

• Leadership Advancement: Potential to move into Director-level roles, managing larger organizations and broader product areas within Google.

• Technical Specialization: Opportunity to deepen expertise in AI, LLMs, and advanced ML techniques, becoming a recognized leader in these fields.

• Cross-Functional Leadership: Exposure to and leadership opportunities within other major Google product areas (e.g., Search, Cloud, Assistant).

• Strategic Influence: Contribute to defining Google's AI strategy for its core platforms and devices.

• Mentorship & Development: Continue to mentor and develop engineering talent, building high-performing teams.

📝 Enhancement Note: This is a high-impact management role within a critical area of Google's business. Growth opportunities are significant, offering paths toward senior leadership and deep technical specialization in AI, aligning with Google's strategic focus.

🌐 Work Environment

Office Type: On-site. This role requires the candidate to work from Google's Mountain View campus.

Office Location(s): Mountain View, California, USA. This is Google's headquarters, offering a vibrant campus environment with extensive amenities.

Workspace Context:

• Collaborative Environment: The campus is designed to foster collaboration, with open workspaces, meeting rooms, and common areas.

• Access to Tools & Technology: Engineers have access to Google's cutting-edge internal tools, infrastructure, and computational resources necessary for AI/ML development.

• Team Interaction: Frequent opportunities for interaction with direct reports, peers, and cross-functional partners through daily stand-ups, team meetings, and informal discussions.

• On-Campus Amenities: Access to Google's renowned campus amenities, including dining facilities, fitness centers, and recreational areas, contributing to a balanced work-life environment.

Work Schedule:

• Standard full-time hours (approximately 40 hours per week) are expected.

• While core hours are important for team collaboration and on-site presence, flexibility may be offered within reasonable limits, subject to team and project needs. Significant availability is expected due to the leadership and on-site requirements.

📝 Enhancement Note: The on-site requirement at Google's Mountain View campus indicates a preference for in-person collaboration, team building, and access to proprietary infrastructure. The environment is designed to support innovation and productivity.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or recruiter screen to assess basic qualifications and alignment with the role.

• Hiring Manager Interview: In-depth discussion about experience, leadership style, technical background, and fit for the team.

• Technical Interviews: Series of interviews focusing on system design, ML concepts, algorithm problem-solving, and potentially coding exercises.

• Leadership/Behavioral Interviews: Assessment of leadership capabilities, team management, conflict resolution, and strategic thinking. This may include scenario-based questions.

• Team/Peer Interviews: Discussions with potential peers or senior engineers on the team to assess collaboration and technical depth.

• Executive/Director Interview: Final interview with a senior leader to evaluate strategic alignment and overall fit.

Portfolio Review Tips:

• Focus on Impact: For each project or experience, clearly articulate the problem, your role, the solution implemented, and the measurable impact (e.g., performance improvements, user adoption, team efficiency).

• Showcase Leadership: Highlight instances where you successfully led teams, mentored engineers, resolved conflicts, or drove strategic initiatives. Use specific examples.

• Technical Depth in AI/ML: Be prepared to discuss your experience with LLMs, ML optimization, and AI agent development in detail. Explain complex concepts clearly.

• Process Improvement: Provide examples of how you've improved engineering processes, workflows, or team productivity.

• Tailor to the Role: Emphasize experiences most relevant to managing AI/ML teams, UI automation, and Android platform development.

Challenge Preparation:

• System Design: Practice designing scalable, distributed systems, with a focus on AI/ML components, real-time processing, and mobile constraints.

• ML/LLM Concepts: Review core ML concepts, LLM architectures, training methodologies, and optimization techniques relevant to on-device deployment.

• Leadership Scenarios: Prepare for questions about managing underperformers, handling team conflict, driving consensus, and making difficult technical decisions.

• Coding Proficiency: Be ready for coding challenges in languages like Python or C++, focusing on algorithmic efficiency and problem-solving.

📝 Enhancement Note: Google's interview process is rigorous and comprehensive, designed to assess a candidate's technical acumen, leadership potential, and cultural fit. A strong portfolio that demonstrates quantifiable achievements in AI/ML leadership and system design is crucial for success.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: C++, Java, Python, Kotlin, Go. Proficiency in Python and C++ is particularly highlighted.

• Machine Learning Frameworks: TensorFlow, PyTorch, JAX (Google's internal ML framework).

• LLM Libraries/APIs: Experience with transformer architectures, Hugging Face, or Google's internal LLM frameworks (e.g., related to Gemma, Gemini).

• Development Environments: Google's internal development tools, IDEs (e.g., CLion, IntelliJ, VS Code).

• Version Control: Git, Perforce (Google's internal VCS).

Analytics & Reporting:

• Data Analysis Tools: Internal Google tools for data processing and analysis (e.g., Colab, BigQuery).

• Performance Monitoring: Tools for monitoring model performance, latency, and resource utilization on device.

• Dashboarding: Internal tools for creating dashboards and visualizing key metrics.

CRM & Automation:

• While not a direct CRM role, understanding how AI agents interact with user interfaces and potential backend systems is key.

• Automation Tools: Experience with scripting, CI/CD pipelines, and workflow automation.

• API Integration: Familiarity with RESTful APIs, gRPC, and other communication protocols for system integration.

• Android Framework: Deep understanding of Android's architecture, components, and development APIs.

📝 Enhancement Note: This role requires a strong command of core programming languages, extensive experience with ML/LLM frameworks, and specialized knowledge of the Android ecosystem. Proficiency in Google's internal tools and infrastructure is often a de facto requirement for roles at Google.

👥 Team Culture & Values

Operations Values:

• Impact: Drive for creating products that positively impact billions of users.

• Innovation: Encourage experimentation and pushing the boundaries of technology, especially in AI.

• Collaboration: Strong emphasis on teamwork, knowledge sharing, and cross-functional partnerships.

• Data-Driven: Decisions are informed by rigorous data analysis and experimentation.

• Excellence: Strive for high-quality, performant, and scalable solutions.

• Inclusivity: Foster a diverse and respectful work environment where all voices are heard.

Collaboration Style:

• Cross-Functional Integration: Seamless collaboration with various product and engineering teams across Google is essential.

• Open Communication: Encouragement of open dialogue, feedback, and constructive debate.

• Mentorship & Knowledge Sharing: A culture where senior engineers actively mentor junior team members and share knowledge broadly.

• Iterative Improvement: Embrace a cycle of building, testing, learning, and iterating to refine products and processes.

📝 Enhancement Note: Google's culture values intellectual curiosity, a results-oriented mindset, and strong interpersonal skills. For this leadership role, demonstrating an ability to foster these values within a team and collaborate effectively across a large organization is paramount.

⚡ Challenges & Growth Opportunities

Challenges:

• Cutting-Edge Technology: Working with rapidly evolving AI and LLM technologies requires continuous learning and adaptation.

• On-Device Optimization: Achieving high performance and low latency for complex LLMs on resource-constrained mobile devices is a significant technical challenge.

• Cross-Functional Alignment: Coordinating efforts across multiple large, complex teams (GDM, Gemma, Gemini, Android) requires strong communication and negotiation skills.

• Defining New Paradigms: Establishing novel AI-driven interactions and UI automation paradigms for mobile presents unique design and implementation hurdles.

• Scalability & Reliability: Ensuring that AI-powered features are robust, reliable, and scalable for billions of users.

Learning & Development Opportunities:

• Access to Google's AI Research: Direct exposure to and potential contribution to Google's leading AI research initiatives.

• Advanced Training: Opportunities for specialized training in AI, ML, NLP, and related fields.

• Industry Conferences: Support for attending and presenting at major AI and software engineering conferences.

• Leadership Development Programs: Google offers various programs to enhance leadership and management skills.

• Exposure to Diverse Technologies: Working across different Google products and platforms provides broad technical exposure.

📝 Enhancement Note: This role offers the chance to tackle some of the most challenging and exciting problems in AI and mobile technology today, with ample opportunities for professional and technical growth.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you led a team through a significant technical challenge. What was your approach, and what was the outcome?" (Focus on leadership, problem-solving, and impact).

• "How would you balance the need for innovation in AI with the requirement for on-device performance and efficiency?" (Focus on strategic trade-offs and technical solutions).

• "Imagine you need to integrate a new LLM capability into Android. Outline the steps you would take, considering technical, product, and team aspects." (Focus on process, cross-functional planning, and execution). Company & Culture Questions:

• "What excites you about Google's mission and its work in AI?" (Demonstrate genuine interest and alignment with Google's values).

• "How do you foster a culture of psychological safety and continuous learning within your engineering teams?" (Assess your people management philosophy).

• "Describe your experience working with cross-functional teams. How do you ensure alignment and drive towards shared goals?" (Highlight collaboration and stakeholder management skills). Portfolio Presentation Strategy:

• Structure: Organize your portfolio around key achievements, using a STAR (Situation, Task, Action, Result) or similar framework for each example.

• Quantify Impact: Use data and metrics to demonstrate the success of your projects and leadership. For AI/ML projects, this could include accuracy improvements, latency reduction, efficiency gains, or user adoption rates.

• Highlight Leadership: For team management examples, focus on how you developed talent, resolved conflicts, and achieved team goals.

• Technical Detail: Be prepared to dive deep into the technical aspects of your projects, especially those involving ML, LLMs, and system design.

• Conciseness: Present your most impactful examples clearly and concisely, leaving time for discussion and follow-up questions.

📝 Enhancement Note: Preparing for Google interviews involves demonstrating a strong blend of technical expertise, leadership capabilities, and cultural alignment. Be ready to articulate your thought process, decision-making rationale, and the impact of your work with data and specific examples.

📌 Application Steps

To apply for this Senior Software Engineering Manager position:

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

• Resume Optimization: Tailor your resume to highlight your extensive experience in software engineering management, technical leadership, people management, and specific expertise in Machine Learning, Large Language Models, and UI automation. Quantify achievements with metrics wherever possible.

• Portfolio Preparation: Curate a portfolio or prepare detailed examples of your past projects that showcase your leadership, technical strategy, team development, and success in AI/ML-related initiatives. Be ready to discuss these in detail during interviews.

• Interview Practice: Thoroughly prepare for technical, behavioral, and system design interviews. Practice articulating your thought process, problem-solving approaches, and leadership philosophy. Focus on how you would apply these to the specific challenges of agentic and UI automation experiences on Android.

• Company Research: Deeply understand Google's mission, its AI strategy, the Platforms and Devices team's role, and the company's culture. Research recent developments in LLMs and UI automation to demonstrate your industry awareness.

⚠️ 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.