Senior Software Engineer, AI/ML GenAI, Google Cloud at Google

📍 Job Overview

Job Title: Senior Software Engineer, AI/ML GenAI, Google Cloud
Company: Google
Location: Sunnyvale, California, United States
Job Type: On-site
Category: Senior Software Engineer
Date Posted: June 19, 2025
Experience Level: 5-10 years
Remote Status: On-site

🚀 Role Summary

Develop and maintain cutting-edge GenAI solutions for Google Cloud, leveraging ML infrastructure and state-of-the-art techniques.
Collaborate with cross-functional teams to design, test, and deploy software products and systems at massive scale.
Contribute to Google's mission to make information universally accessible and useful, while ensuring the responsible development and use of AI technologies.

📝 Enhancement Note: This role requires a strong background in software development, GenAI techniques, and ML infrastructure. Familiarity with Google's products and services, as well as its mission and values, will be beneficial for success in this role.

💻 Primary Responsibilities

Software Development: Write, test, and maintain product or system development code, ensuring best practices and high-quality standards.
Collaboration: Work with peers and stakeholders through design and code reviews, contributing to documentation and educational content.
Issue Resolution: Triage, debug, and resolve product or system issues, analyzing their impact on hardware, network, or service operations and quality.
GenAI Solution Design: Design and implement GenAI solutions, leveraging ML infrastructure and evaluating tradeoffs between different techniques and their application domains.
ML Infrastructure: Develop, maintain, and optimize ML infrastructure for model deployment, evaluation, optimization, data processing, and debugging.

📝 Enhancement Note: This role requires a strong focus on problem-solving, collaboration, and attention to detail. Experience with large-scale systems, distributed computing, and data storage will be valuable for success in this role.

🎓 Skills & Qualifications

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

Experience:

5+ years of experience with software development in one or more programming languages, and with data structures/algorithms.
3+ years of experience with state-of-the-art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
3+ years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
3+ years of experience testing, maintaining, or launching software products.
1+ year of experience with software design and architecture.

Required Skills:

Proficiency in one or more programming languages (e.g., Python, Java, C++).
Strong understanding of data structures and algorithms.
Experience with GenAI techniques and ML infrastructure.
Familiarity with software testing, debugging, and issue resolution.
Excellent collaboration and communication skills.

Preferred Skills:

Master's degree or PhD in Computer Science or a related technical field.
Experience in a technical leadership role.
Familiarity with accessible technology development.

📝 Enhancement Note: Candidates with experience in large-scale system design, networking, and data storage may have an advantage in this role. Familiarity with Google's products, services, and culture will also be beneficial.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A portfolio showcasing your software development projects, with a focus on GenAI solutions and ML infrastructure implementation.
Live demos or case studies demonstrating your problem-solving skills, performance optimization, and user experience design.
Examples of your collaboration and communication skills, such as code reviews, documentation, or team projects.

Technical Documentation:

Well-commented and well-documented code, adhering to Google's coding standards and best practices.
Clear and concise project documentation, including system design, architecture decisions, and testing methodologies.
Evidence of your ability to evaluate and optimize ML models, as well as your understanding of data processing and debugging techniques.

📝 Enhancement Note: For this role, it's essential to highlight your experience with GenAI techniques, ML infrastructure, and software design. Include examples of your ability to collaborate with cross-functional teams and contribute to documentation and educational content.

💵 Compensation & Benefits

Salary Range: The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

Benefits:

Bonus
Equity
Benefits (including health, dental, and vision insurance, 401(k) matching, and other perks)

Working Hours: 40 hours per week, with flexible working hours and the opportunity to work from home occasionally.

📝 Enhancement Note: The provided salary range is determined by Google's compensation structure, which considers role, level, and location. Individual pay may vary based on the factors mentioned above.

🎯 Team & Company Context

🏢 Company Culture

Industry: Google operates in the technology industry, with a focus on search, advertising, and cloud computing. Its mission is to make information universally accessible and useful.

Company Size: Google is a large, multinational corporation with over 135,000 employees worldwide. This size offers opportunities for career growth, diverse projects, and collaboration with experts in various fields.

Founded: Google was founded in 1998 by Larry Page and Sergey Brin. It has since grown into one of the world's most influential technology companies.

Team Structure:

The Google Cloud team consists of various sub-teams, including AI/ML, Infrastructure, and Product Management.
The AI/ML team is further divided into specialized groups focused on different aspects of AI and machine learning, such as GenAI, Natural Language Processing, and Computer Vision.
The team follows a flat structure, with a focus on collaboration, innovation, and continuous learning.

Development Methodology:

Google follows Agile development methodologies, with a focus on iterative development, continuous integration, and collaboration.
The company uses a combination of in-house and external tools for version control, project management, and communication.
Google encourages a culture of experimentation, with a focus on testing, learning, and iterating on new ideas.

Company Website: Google Careers

📝 Enhancement Note: Google's company culture is characterized by its mission-driven approach, focus on innovation, and emphasis on collaboration and teamwork. The company values diversity, inclusion, and continuous learning, providing opportunities for employees to grow both personally and professionally.

📈 Career & Growth Analysis

Web Technology Career Level: This role is a senior-level position, requiring a strong background in software development, GenAI techniques, and ML infrastructure. It offers opportunities for technical leadership, mentorship, and career progression within Google's AI/ML and cloud computing teams.

Reporting Structure: This role reports directly to the engineering manager of the AI/ML GenAI team within Google Cloud. The team consists of various sub-teams, with opportunities for collaboration and cross-functional projects.

Technical Impact: As a senior software engineer, you will have a significant impact on Google Cloud's AI/ML products and services. Your work will contribute to the development and deployment of state-of-the-art GenAI solutions, as well as the optimization and maintenance of ML infrastructure.

Growth Opportunities:

Technical Leadership: Opportunities to mentor junior engineers, lead projects, and contribute to architecture and design decisions.
Specialization: The chance to specialize in specific areas of AI/ML, such as GenAI, Natural Language Processing, or Computer Vision.
Career Progression: Opportunities to advance to roles such as Staff Software Engineer, Engineering Manager, or Technical Lead within Google's AI/ML and cloud computing teams.

📝 Enhancement Note: Google's career progression is based on a combination of individual performance, team success, and the needs of the organization. Employees are encouraged to take on new challenges, learn new skills, and grow both personally and professionally.

🌐 Work Environment

Office Type: Google's Sunnyvale office is a modern, collaborative workspace designed to foster innovation and creativity. It features open-plan workspaces, meeting rooms, and recreational areas, as well as on-site amenities such as cafes, fitness centers, and wellness spaces.

Office Location(s): Sunnyvale, California, United States. Google has multiple offices worldwide, with opportunities for relocation and remote work in some cases.

Workspace Context:

Collaboration: Google's office environment encourages collaboration and teamwork, with open-plan workspaces and dedicated meeting rooms for team discussions and brainstorming sessions.
Technology: The office is equipped with state-of-the-art technology, including high-speed internet, multiple monitors, and testing devices to support software development and debugging.
Flexibility: Google offers flexible working hours and the opportunity to work from home occasionally, allowing employees to balance their personal and professional lives.

Work Schedule: Google offers a flexible work schedule, with core hours between 10:00 AM and 4:00 PM. Employees are expected to work a minimum of 40 hours per week, with the opportunity to work from home occasionally.

📝 Enhancement Note: Google's work environment is designed to support collaboration, innovation, and work-life balance. The company offers a range of benefits and perks to support employee well-being and productivity.

📄 Application & Technical Interview Process

Interview Process:

Phone Screen (30-45 minutes): A brief conversation with a recruiter or hiring manager to discuss your background, experience, and motivation for the role.
Technical Phone Screen (45-60 minutes): A technical conversation with an engineer or hiring manager to assess your problem-solving skills, coding ability, and understanding of GenAI techniques and ML infrastructure.
On-site Interview (4-6 hours): A series of interviews with engineers, hiring managers, and other stakeholders to assess your technical skills, cultural fit, and alignment with Google's mission and values.
- Coding Challenge (60-90 minutes): A hands-on coding challenge or system design exercise to evaluate your problem-solving skills and ability to work under pressure.
- Behavioral Questions (60-90 minutes): A conversation focused on your past experiences, accomplishments, and approach to problem-solving and collaboration.
- Architecture & Design (60-90 minutes): A discussion focused on your understanding of software architecture, design patterns, and tradeoffs between different techniques and their application domains.
- Cultural Fit (30-45 minutes): A conversation focused on your alignment with Google's mission, values, and company culture.
Final Decision: A final decision is made based on your performance throughout the interview process, as well as your alignment with the role, team, and company.

Portfolio Review Tips:

Highlight your experience with GenAI techniques, ML infrastructure, and software design.
Include examples of your ability to collaborate with cross-functional teams and contribute to documentation and educational content.
Showcase your problem-solving skills, performance optimization, and user experience design.

Technical Challenge Preparation:

Brush up on your knowledge of GenAI techniques, ML infrastructure, and software design principles.
Practice coding challenges and system design exercises to improve your problem-solving skills and ability to work under pressure.
Familiarize yourself with Google's products, services, and company culture to demonstrate your alignment with the company's mission and values.

ATS Keywords:

Programming Languages: Python, Java, C++, JavaScript, Go, R
Web Frameworks: Flask, Django, TensorFlow, PyTorch, Keras
Server Technologies: Kubernetes, Docker, Google Cloud Platform, AWS, GCP
Databases: BigQuery, Cloud Spanner, Firestore, MongoDB, PostgreSQL
Tools: Git, Jupyter Notebooks, TensorBoard, MLflow, Kubeflow
Methodologies: Agile, Scrum, Kanban, CI/CD, DevOps
Soft Skills: Problem-solving, Collaboration, Communication, Leadership, Mentoring
Industry Terms: AI/ML, GenAI, LLMs, Multi-Modal, Large Vision Models, Language Modeling, Computer Vision, Model Deployment, Model Evaluation, Optimization, Data Processing, Debugging

📝 Enhancement Note: The interview process for this role is designed to assess your technical skills, problem-solving abilities, and cultural fit with Google's mission, values, and company culture. Preparation and practice are essential for success in the technical phone screen, coding challenge, and architecture & design interviews.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies:

Web Frameworks: React, Angular, Vue.js
Libraries & Tools: Redux, NgRx, Vuex, Webpack, Babel
State Management: MobX, Akita, Nx
Testing: Jest, Mocha, Jasmine, Cypress, Selenium

Backend & Server Technologies:

Programming Languages: Python, Java, C++, Go, Node.js
Frameworks & Libraries: Flask, Django, Spring Boot, Express.js, FastAPI
Databases: PostgreSQL, MySQL, MongoDB, Redis, Cassandra
Server Platforms: Kubernetes, Docker, AWS, GCP, Azure
Cloud Platforms: Google Cloud Platform, AWS, GCP, Azure

Development & DevOps Tools:

Version Control: Git, SVN, Mercurial
CI/CD Pipelines: Jenkins, CircleCI, GitHub Actions, GitLab CI/CD
Containerization: Docker, Kubernetes, Amazon ECS, Google Kubernetes Engine
Monitoring & Logging: Prometheus, Grafana, ELK Stack, Datadog, New Relic
Infrastructure as Code (IaC): Terraform, CloudFormation, Pulumi, Ansible, Puppet

📝 Enhancement Note: Google uses a wide range of technologies and tools for software development, ML infrastructure, and cloud computing. Familiarity with these technologies and tools will be beneficial for success in this role.

👥 Team Culture & Values

Web Development Values:

Innovation: Google values innovation and experimentation, encouraging employees to take risks and learn from failures.
Collaboration: Google fosters a culture of collaboration and teamwork, with a focus on open communication, knowledge sharing, and continuous learning.
User-Centric: Google prioritizes the user experience, with a focus on accessibility, performance, and usability.
Quality: Google is committed to delivering high-quality products and services, with a focus on testing, quality assurance, and continuous improvement.

Collaboration Style:

Cross-Functional Integration: Google encourages collaboration between different teams and disciplines, with a focus on user-centered design and user experience.
Code Review Culture: Google values code reviews as a means of knowledge sharing, learning, and improving the quality of its products and services.
Peer Programming: Google encourages peer programming and pair coding as a means of learning, collaboration, and knowledge sharing.

📝 Enhancement Note: Google's team culture is characterized by its focus on innovation, collaboration, and user-centric design. The company values experimentation, learning, and continuous improvement, providing opportunities for employees to grow both personally and professionally.

⚡ Challenges & Growth Opportunities

Technical Challenges:

GenAI Techniques: Staying up-to-date with the latest developments in GenAI techniques, such as LLMs, Multi-Modal, and Large Vision Models.
ML Infrastructure: Designing, implementing, and optimizing ML infrastructure for model deployment, evaluation, optimization, data processing, and debugging.
Performance Optimization: Evaluating and optimizing ML models, as well as the performance of software products and systems at massive scale.
User Experience: Designing and implementing user-centered design principles, with a focus on accessibility, performance, and usability.

Learning & Development Opportunities:

Technical Skill Development: Opportunities to learn new technologies, tools, and methodologies through training, workshops, and online resources.
Conference Attendance: Google encourages employees to attend industry conferences, meetups, and events to stay up-to-date with the latest developments in AI/ML and cloud computing.
Mentorship & Leadership: Opportunities to mentor junior engineers, lead projects, and contribute to architecture and design decisions.

📝 Enhancement Note: Google offers numerous opportunities for learning, growth, and development, with a focus on technical skill development, mentorship, and leadership. The company encourages employees to take on new challenges, learn new skills, and grow both personally and professionally.

💡 Interview Preparation

Technical Questions:

GenAI Techniques (30-45 minutes): A technical conversation focused on your understanding of GenAI techniques, such as LLMs, Multi-Modal, and Large Vision Models. You may be asked to explain your approach to designing, implementing, and optimizing GenAI solutions.
ML Infrastructure (30-45 minutes): A technical conversation focused on your understanding of ML infrastructure, including model deployment, evaluation, optimization, data processing, and debugging. You may be asked to describe your approach to designing, implementing, and optimizing ML infrastructure for GenAI solutions.
System Design (30-45 minutes): A technical conversation focused on your understanding of software architecture, design patterns, and tradeoffs between different techniques and their application domains. You may be asked to discuss your approach to designing, implementing, and optimizing software products and systems at massive scale.

Company & Culture Questions (30-45 minutes): A conversation focused on your alignment with Google's mission, values, and company culture. You may be asked to discuss your approach to collaboration, innovation, and user-centered design, as well as your understanding of Google's products, services, and industry.

Portfolio Presentation Strategy:

Live Demo (15-30 minutes): A live demo of your software development projects, with a focus on GenAI solutions and ML infrastructure implementation. Highlight your problem-solving skills, performance optimization, and user experience design.
Code Walkthrough (15-30 minutes): A walkthrough of your code, with an emphasis on your approach to software design, architecture, and best practices. Explain your decision-making process and tradeoffs between different techniques and their application domains.
Q&A (15-30 minutes): A question-and-answer session focused on your understanding of Google's products, services, and industry. Demonstrate your ability to think critically, problem-solve, and collaborate with cross-functional teams.

📝 Enhancement Note: The interview process for this role is designed to assess your technical skills, problem-solving abilities, and cultural fit with Google's mission, values, and company culture. Preparation and practice are essential for success in the technical phone screen, coding challenge, and architecture & design interviews.

📌 Application Steps

To apply for this Senior Software Engineer, AI/ML GenAI, Google Cloud position:

Submit Your Application: Visit the Google Careers website and search for the job title "Senior Software Engineer, AI/ML GenAI, Google Cloud." Click on the job listing and follow the instructions to submit your application.
Prepare Your Portfolio: Tailor your portfolio to highlight your experience with GenAI techniques, ML infrastructure, and software design. Include examples of your ability to collaborate with cross-functional teams and contribute to documentation and educational content.
Optimize Your Resume: Highlight your relevant experience, skills, and accomplishments in your resume. Include keywords related to GenAI techniques, ML infrastructure, and software development to improve your chances of being noticed by Google's Applicant Tracking System (ATS).
Prepare for Technical Interviews: Brush up on your knowledge of GenAI techniques, ML infrastructure, and software design principles. Practice coding challenges and system design exercises to improve your problem-solving skills and ability to work under pressure. Familiarize yourself with Google's products, services, and company culture to demonstrate your alignment with the company's mission and values.
Research Google: Learn about Google's products, services, and industry to demonstrate your understanding of the company and its role in the technology sector. Prepare thoughtful questions to ask during your interviews, focusing on Google's mission, values, and company culture.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Senior Software Engineer, AI/ML GenAI, Google Cloud