Senior Software Engineer, AI/ML, Ads UI Experiences

📍 Job Overview

Job Title: Senior Software Engineer, AI/ML, Ads UI Experiences

Company: Google

Location: Mountain View, California, United States

Job Type: Full-Time

Category: Software Engineering / AI/ML / Advertising Technology

Date Posted: January 19, 2026

Experience Level: 5-10 Years

Remote Status: On-site

🚀 Role Summary

• Develop and deploy advanced AI/ML models within the Google Ads user interface, focusing on enhancing user experiences through personalized recommendations and intelligent features.

• Drive innovation in the application of Machine Learning infrastructure, including model deployment, optimization, and performance monitoring for large-scale advertising platforms.

• Collaborate with cross-functional teams to translate complex business requirements into robust, scalable software solutions that impact billions of users globally.

• Contribute to the full software development lifecycle, from design and architecture to testing, maintenance, and launch of critical advertising technology products.

📝 Enhancement Note: This role is positioned for a senior software engineer with a strong specialization in AI/ML and a focus on user-facing product development within the advertising technology domain. The emphasis on "UI Experiences" and "Ads" indicates a need for engineers who can bridge the gap between complex machine learning algorithms and intuitive, high-impact user interfaces. The "Senior" title implies a significant level of autonomy, technical leadership, and strategic contribution is expected.

📈 Primary Responsibilities

• Write, test, and maintain high-quality product or system development code in languages such as C++, Java, Kotlin, and TypeScript, adhering to Google's rigorous engineering standards.

• Design, implement, and deploy sophisticated recommendation system models (including retrieval, prediction, ranking, personalization, search quality, and embedding) into production environments.

• Leverage and contribute to ML infrastructure, focusing on efficient model deployment, thorough model evaluation, performance optimization, robust data processing, and effective debugging strategies.

• Collaborate actively with cross-functional peers, product managers, and stakeholders through design and code reviews, ensuring adherence to best practices in software design, architecture, testability, and efficiency.

• Triage, debug, and resolve complex product or system issues by analyzing root causes and their impact on hardware, network, or service operations and overall quality.

• Contribute to existing documentation and educational content, adapting materials based on product/program updates and user feedback to ensure clarity and utility for internal and external users.

• Participate in architectural discussions and contribute to the design of scalable, distributed systems that handle massive amounts of data for advertising products.

📝 Enhancement Note: The responsibilities highlight a hands-on engineering role that requires deep technical expertise in both software development and machine learning. The emphasis on "triaging," "debugging," and "resolving issues" points to the need for strong problem-solving skills and a proactive approach to maintaining system stability and performance in a high-stakes advertising environment.

🎓 Skills & Qualifications

Education:

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

Experience:

• A minimum of 5 years of professional software development experience, demonstrating proficiency in at least C++, Java, Kotlin, and TypeScript.

• At least 3 years of hands-on experience building and deploying recommendation systems models (e.g., retrieval, prediction, ranking, personalization, search quality, embedding) in a production setting, including experience with architectural design.

• A minimum of 3 years of experience working with ML infrastructure, covering aspects such as model deployment, model evaluation, optimization techniques, data processing pipelines, and debugging ML systems.

Required Skills:

• Proficiency in multiple programming languages: C++, Java, Kotlin, TypeScript.

• Deep understanding and practical experience with Machine Learning concepts and building production-ready recommendation systems.

• Expertise in ML infrastructure, including model deployment pipelines, evaluation metrics, and optimization strategies.

• Strong analytical and debugging skills to identify and resolve complex software and system issues.

• Proven ability to collaborate effectively with engineering teams and stakeholders through design and code reviews.

Preferred Skills:

• Experience with Large Language Model (LLM) products, including prompt engineering and quality evaluation methodologies.

• Demonstrated ability to analyze complex data and derive unique, actionable insights.

• A track record of thriving in dynamic, ambiguous, and fast-paced technical environments.

• Excellent communication, collaboration, and stakeholder management skills.

• Exceptional investigative, problem-solving, and debugging capabilities.

📝 Enhancement Note: The requirements clearly differentiate between minimum and preferred qualifications, indicating a strong emphasis on practical, hands-on experience in ML model deployment and recommendation systems. The preference for advanced degrees and LLM experience suggests that Google is looking for candidates who can push the boundaries of AI/ML within their advertising products. The blend of technical skills and soft skills (communication, problem-solving, ambiguity tolerance) is crucial for success at Google.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate projects showcasing the end-to-end development and deployment of ML models, particularly recommendation systems, with clear explanations of architectural choices and their impact.

• Provide examples of code contributions or personal projects that highlight proficiency in C++, Java, Kotlin, or TypeScript, focusing on maintainability, efficiency, and scalability.

• Include case studies or project descriptions detailing experience with ML infrastructure, such as model serving frameworks, data pipelines, and performance monitoring tools.

Process Documentation:

• Be prepared to discuss your approach to documenting ML model development processes, from data preprocessing and feature engineering to model training, evaluation, and deployment.

• Highlight your experience in establishing and adhering to best practices for software development, including testing strategies, version control, and continuous integration/continuous deployment (CI/CD) pipelines relevant to ML workflows.

• Be ready to explain how you measure the performance and impact of ML models in production, including metrics for accuracy, latency, throughput, and business impact.

📝 Enhancement Note: While a formal "portfolio" might not be explicitly requested in every application, candidates are strongly advised to prepare examples of their work that align with these requirements. This could include a GitHub profile, personal projects, or detailed descriptions of professional achievements that demonstrate the listed skills and experience. For an AI/ML role at Google, showcasing practical application of ML concepts and infrastructure is paramount.

💵 Compensation & Benefits

Salary Range:

• The US base salary range for this full-time position is $166,000 - $244,000 per year.

Benefits:

• Bonus: Performance-based bonus opportunities.

• Equity: Stock options or grants as part of the compensation package.

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

• Retirement Plan: 401(k) plan with company matching.

• Additional Benefits: Access to Google's extensive benefits program, which typically includes paid time off, parental leave, wellness programs, employee assistance programs, and various perks.

Working Hours:

• This is a full-time position, typically requiring approximately 40 hours per week.

• Flexibility may be available, but the role is designated as on-site, implying a need for consistent presence during core business hours.

📝 Enhancement Note: The provided salary range is a US base salary only and does not include bonus, equity, or other benefits. Google's compensation philosophy is typically total rewards, meaning the overall package (base salary + bonus + equity + benefits) is highly competitive. The range provided is for a Senior Software Engineer, reflecting significant experience and expertise. The recruiter will provide a more specific range based on the candidate's location and the exact leveling during the hiring process.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology (Internet Services, Advertising, AI/ML)

• Google operates at the forefront of technological innovation, driving advancements in search, cloud computing, AI, and digital advertising. The company is known for its data-driven decision-making, focus on user experience, and commitment to solving complex global challenges through technology. The advertising technology sector within Google is highly competitive, fast-paced, and constantly evolving with new platforms and user engagement strategies.

Company Size: 100,000+ Employees

• As one of the largest technology companies globally, Google offers immense resources, opportunities for specialization, and the chance to work on products that impact billions. The scale of operations means established processes, but also a need for engineers who can navigate large organizations and contribute effectively within specialized teams.

Founded: 1998

Team Structure:

• Operations Team Aspect 1: This role is part of the Google Ads engineering team, which is comprised of numerous specialized sub-teams focusing on different aspects of the advertising ecosystem, including search, display, shopping, video, analytics, and UI experiences. The AI/ML team is likely a central component, supporting various product areas.

• Operations Team Aspect 2: Engineers typically report to a Software Engineering Manager, who in turn reports to a Director or VP. Within the Ads UI Experiences team, there will be a direct reporting line focused on AI/ML development and product integration.

• Operations Team Aspect 3: Collaboration is a cornerstone of Google's engineering culture. This role will involve close interaction with product managers, UX designers, other software engineers (front-end, back-end, ML), and potentially researchers to define requirements, design solutions, and integrate AI/ML features seamlessly into the Ads user interface.

Methodology:

• Operations Process 1: Google heavily relies on data analysis and experimentation (A/B testing) to inform product decisions and measure the impact of new features. Engineers are expected to leverage data to understand user behavior, system performance, and the effectiveness of ML models.

• Operations Process 2: Workflow planning and optimization are critical due to the scale. Agile methodologies are often adapted, with a strong emphasis on iterative development, code reviews, and continuous feedback loops to ensure efficient progress and high-quality output.

• Operations Process 3: Automation is a key driver of efficiency at Google. Engineers are expected to automate testing, deployment, monitoring, and repetitive tasks using internal tools and infrastructure to enable faster iteration and reduce manual effort.

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

📝 Enhancement Note: Understanding Google's culture of innovation, data-driven decision-making, and emphasis on collaboration is crucial. The Ads UI Experiences team specifically bridges cutting-edge AI/ML with user-facing product design, requiring engineers who are both technically adept and user-centric. The scale of Google Ads means that even minor improvements can have a massive impact.

📈 Career & Growth Analysis

Operations Career Level: Senior Software Engineer

Reporting Structure:

Operations Impact:

Growth Opportunities:

• Operations Skill Advancement: Deepen expertise in AI/ML, recommendation systems, LLMs, and large-scale distributed systems. Opportunity to learn and apply cutting-edge research into production.

• Technical Leadership: Progress to Staff, Senior Staff, or Principal Engineer roles, taking on greater technical scope, architectural ownership, and mentorship responsibilities.

• Management Track: Potential to move into management roles, leading engineering teams and guiding product strategy.

• Cross-Functional Mobility: Opportunities to explore other product areas within Google or specialize further within the Ads ecosystem, leveraging acquired skills.

📝 Enhancement Note: A "Senior" title at Google implies a high level of technical proficiency and leadership potential. Candidates should be prepared to discuss their contributions to past projects, their ability to mentor, and their strategic thinking regarding technical challenges and solutions. The growth opportunities highlight Google's commitment to internal development and career progression within its engineering ranks.

🌐 Work Environment

Office Type: Primarily On-site with some flexibility.

Office Location(s):

• Mountain View, California, United States: This is the location for the role, situated in the heart of Silicon Valley. The Google campus offers state-of-the-art facilities, including collaborative workspaces, cafes, fitness centers, and other amenities designed to support employee well-being and productivity.

Workspace Context:

• Workspace Aspect 1: The environment is highly collaborative, with teams often working in open-plan spaces or dedicated project areas. Expect frequent interaction with colleagues, including pair programming, design discussions, and team meetings.

• Workspace Aspect 2: Access to Google's extensive internal tools, robust cloud infrastructure, high-performance computing resources, and cutting-edge hardware is standard. This includes proprietary ML platforms, data processing frameworks, and development environments.

• Workspace Aspect 3: Opportunities for informal and formal interactions with engineers, product managers, designers, and researchers across various teams. This facilitates knowledge sharing, problem-solving, and exposure to different areas of Google's technology stack.

Work Schedule:

• Standard full-time hours are expected, typically around 40 hours per week. While Google often promotes a healthy work-life balance, the demands of a Senior Engineer role on a critical product like Google Ads can involve periods of intense work, especially around launch cycles or critical issue resolution. Flexibility in daily start/end times may be possible, subject to team and manager approval.

📝 Enhancement Note: The on-site requirement emphasizes Google's value for in-person collaboration and the benefits of a shared physical workspace for innovation and team cohesion. Candidates should be prepared to discuss how they thrive in such an environment and contribute to a collaborative team dynamic.

📄 Application & Portfolio Review Process

Interview Process:

• Process Step 1: Initial Screening: A recruiter will review your application, potentially followed by a brief phone screen to assess basic qualifications and interest.

• Process Step 2: Technical Phone Screens: Typically 1-2 interviews focusing on core computer science fundamentals, data structures, algorithms, and potentially basic ML concepts. Candidates are often asked to solve coding problems on a shared editor.

• Process Step 3: On-site/Virtual On-site Interviews: A series of 4-6 interviews, often including:

  • Coding interviews (focus on algorithm, data structure, and problem-solving).
  • System Design interviews (focus on designing scalable systems, potentially including ML system design).
  • ML/AI interviews (focus on ML concepts, model building, evaluation, and infrastructure).
  • Behavioral interviews (assessing leadership, collaboration, problem-solving, ambiguity tolerance, and cultural fit).

• Process Step 4: Hiring Committee Review: Interview feedback is compiled and reviewed by a hiring committee to make a final decision. This committee ensures consistency and fairness across all candidate evaluations.

Portfolio Review Tips:

• Portfolio Tip 1: Curate examples of your most impactful ML projects, especially those involving recommendation systems or UI integration. Clearly articulate the problem, your solution, the technologies used, and the quantifiable results.

• Portfolio Tip 2: For each project, structure your explanation using the STAR method (Situation, Task, Action, Result). Quantify your contributions and the impact of your work using metrics.

• Portfolio Tip 3: Be ready to discuss your code and design choices in detail. Highlight your understanding of trade-offs, scalability considerations, and best practices in ML development and deployment.

• Portfolio Tip 4: Showcase any experience with prompt engineering, LLMs, or evaluating AI model quality. Demonstrate your ability to think critically about the ethical implications and potential biases in AI systems.

Challenge Preparation:

• Challenge Preparation 1: Practice coding problems extensively on platforms like LeetCode, focusing on medium to hard difficulty. Be comfortable with common algorithms and data structures.

• Challenge Preparation 2: Prepare for system design questions by studying common architectural patterns for distributed systems, databases, caching, and ML pipelines. Practice drawing diagrams and explaining trade-offs.

• Challenge Preparation 3: Review core ML concepts, including supervised/unsupervised learning, common algorithms (e.g., decision trees, neural networks, SVMs), evaluation metrics (precision, recall, F1, AUC), and regularization techniques. Understand the lifecycle of an ML model.

• Challenge Preparation 4: Prepare specific examples from your experience that demonstrate your problem-solving skills, leadership, ability to handle ambiguity, and collaboration with diverse teams.

📝 Enhancement Note: Google's interview process is rigorous and aims to assess a candidate's technical depth, problem-solving abilities, and cultural fit. Candidates should prepare thoroughly across all interview types. Portfolio readiness is key, as interviewers will probe deeply into past projects.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: C++, Java, Kotlin, TypeScript (core languages for development).

• ML Frameworks: TensorFlow, PyTorch, JAX (used for building and training ML models).

• Data Processing: Apache Beam, Apache Spark, Google Cloud Dataflow (for large-scale data pipelines).

• Version Control: Git (standard for code management).

Analytics & Reporting:

• Internal Google Tools: Extensive proprietary tools for logging, monitoring, A/B testing (e.g., internal experimentation platforms), and data analysis.

• Data Warehousing: Google BigQuery (for querying and analyzing massive datasets).

• Visualization: Internal dashboards and potentially tools like Tableau or Looker for data visualization and reporting.

CRM & Automation:

• CRM: While not directly a CRM role, understanding how user data is managed in systems like Google Ads is important.

• Automation: Internal Google CI/CD tools, scripting languages (Python), and workflow orchestration tools for automating model deployment, testing, and monitoring.

• Cloud Platform: Google Cloud Platform (GCP) services, including AI Platform, Compute Engine, Cloud Storage, Kubernetes Engine (GKE).

📝 Enhancement Note: Proficiency in Google's internal tools and infrastructure is developed on the job. However, a strong foundation in common industry-standard tools like TensorFlow, PyTorch, Spark, BigQuery, and Git is highly valuable and often a prerequisite. Emphasis is placed on practical application within Google's scalable, cloud-native environment.

👥 Team Culture & Values

Operations Values:

• Operations Value 1: Focus on the user and all else will follow: This principle drives the development of user-centric AI/ML features that enhance advertiser and user experiences within Google Ads.

• Operations Value 2: It's best to do one thing really, really well: This encourages deep specialization and mastery in areas like AI/ML and recommendation systems, fostering expertise within the team.

• Operations Value 3: Fast is better than slow: This value promotes agile development, rapid iteration, and efficient deployment of new features and model improvements to stay ahead in the competitive ad-tech landscape.

• Operations Value 4: Great just isn't good enough: This fosters a culture of continuous improvement, pushing for innovation and excellence in every aspect of engineering, from code quality to model performance.

Collaboration Style:

• Collaboration Approach 1: Highly collaborative, with engineers working closely with product managers, UX designers, and fellow engineers to define, build, and launch features. Cross-functional alignment is critical.

• Collaboration Approach 2: Emphasis on open communication, constructive feedback through code reviews, and shared ownership of projects. Teams often engage in regular sync-ups and planning sessions.

• Collaboration Approach 3: Knowledge sharing is encouraged through internal tech talks, documentation, and mentorship. Engineers are expected to learn from each other and contribute to the collective knowledge base.

📝 Enhancement Note: Google's core values are deeply ingrained in its culture. For this role, understanding how these values translate into the daily work of an AI/ML engineer in the Ads domain—prioritizing user experience through technology, striving for deep technical excellence, and maintaining a pace of rapid innovation—is important for cultural fit.

⚡ Challenges & Growth Opportunities

Challenges:

• Operations Challenge 1: Developing and deploying AI/ML models at Google's scale presents unique engineering challenges related to data volume, computational resources, latency requirements, and model drift. Mitigation involves leveraging Google's robust infrastructure and best practices.

• Operations Challenge 2: The advertising technology landscape is highly dynamic, with constant shifts in user behavior, platform policies, and competitive pressures. Staying current with these changes and adapting ML models and UI experiences accordingly is an ongoing challenge.

• Operations Challenge 3: Balancing the complexity of advanced AI/ML algorithms with the need for simple, intuitive user interfaces requires careful design and iterative refinement. Ensuring that sophisticated technology translates into a positive user experience is key.

• Operations Challenge 4: Integrating new AI advancements, such as LLMs, into existing complex systems like Google Ads requires careful planning, rigorous testing, and strategic implementation to ensure reliability and effectiveness.

Learning & Development Opportunities:

• Learning Opportunity 1: Access to Google's internal AI/ML research, extensive training resources, and opportunities to work with world-class experts in the field.

• Learning Opportunity 2: Participation in industry conferences, workshops, and internal tech talks to stay abreast of the latest trends in AI, ML, and advertising technology.

• Learning Opportunity 3: Mentorship programs and opportunities to lead technical initiatives, fostering growth towards Staff Engineer, Principal Engineer, or management roles.

📝 Enhancement Note: The challenges inherent in this role are significant but also offer immense opportunities for professional growth. Candidates who are motivated by complex problems, rapid learning, and making a global impact will find this role rewarding.

💡 Interview Preparation

Strategy Questions:

• Operations Question 1: "Describe a complex ML system you designed or significantly contributed to. What were the key architectural decisions, and what trade-offs did you make?" (Focus on explaining your design process, understanding of distributed systems, and justification for choices).

• Operations Question 2: "How would you approach building a recommendation system for Google Ads that balances relevance, diversity, and advertiser goals? What data would you use, and how would you measure success?" (Prepare to discuss various recommendation algorithms, feature engineering, evaluation metrics, and business impact).

• Operations Question 3: "Imagine a critical ML model deployed in Google Ads is showing degraded performance. How would you debug and resolve this issue?" (Demonstrate a systematic debugging approach, covering data issues, model drift, infrastructure problems, and potential code bugs).

Company & Culture Questions:

• Operations Question 4: "Why are you interested in working on AI/ML for Google Ads UI Experiences specifically?" (Connect your skills and interests to the role's impact on users and the advertising ecosystem).

• Operations Question 5: "Describe a time you had to collaborate with engineers from different disciplines (e.g., front-end, PMs) to deliver a product. What were the challenges, and how did you overcome them?" (Highlight your communication and teamwork skills).

• Operations Question 6: "How do you stay up-to-date with the rapidly evolving field of AI/ML?" (Show initiative and a passion for continuous learning).

Portfolio Presentation Strategy:

• Presentation Strategy 1: Be ready to walk through 1-2 key projects from your resume or portfolio. For each, clearly articulate the problem, your role, the technical solution (focusing on ML/AI aspects), and the quantifiable impact.

• Presentation Strategy 2: Practice explaining complex technical concepts in a clear, concise manner, suitable for an audience that may include both technical and non-technical interviewers.

• Presentation Strategy 3: For system design questions, use a whiteboard or virtual equivalent to draw diagrams as you explain your thought process. Discuss scalability, reliability, and trade-offs.

• Presentation Strategy 4: Be prepared to discuss your experience with specific technologies mentioned in the job description (C++, Java, Kotlin, TypeScript, ML frameworks) and how you've used them in practice.

📝 Enhancement Note: Preparation should focus on demonstrating not only technical expertise but also a strong understanding of Google's culture, problem-solving methodologies, and the specific challenges and opportunities within the Ads UI Experiences domain. Quantifying impact and articulating thought processes are key.

📌 Application Steps

To apply for this operations position:

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

• Resume Optimization: Tailor your resume to highlight specific experience with C++, Java, Kotlin, TypeScript, recommendation systems, ML infrastructure, and large-scale software development. Quantify achievements with metrics wherever possible.

• Portfolio Preparation: Prepare detailed descriptions of 2-3 key projects that showcase your AI/ML and software engineering skills, focusing on impact and technical execution. Be ready to discuss them in depth.

• Technical Practice: Dedicate significant time to practicing coding problems (LeetCode), system design, and ML-specific interview questions. Review fundamental CS concepts and ML principles.

• Company Research: Familiarize yourself with Google's mission, values, and the Google Ads product. Understand the challenges and opportunities in the ad-tech industry.

⚠️ 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.