📍 Job Overview

Job Title: Senior Software Engineer, AI/ML, Google Cloud Compute
Company: Google
Location: Kirkland, WA, USA
Job Type: On-site
Category: Senior Software Engineer
Date Posted: June 25, 2025
Experience Level: 5-10 years

🚀 Role Summary

AI/ML Focus: Develop and maintain AI/ML models and infrastructure for Google Cloud Compute, with a focus on speech/audio technology, reinforcement learning, or other ML specializations.
Full-Stack Development: Collaborate with cross-functional teams to design, develop, test, and deploy software products, ensuring high-quality and efficient code.
ML Infrastructure Expertise: Leverage ML infrastructure to deploy, evaluate, and optimize models, with a strong focus on data processing, debugging, and model evaluation.
Technical Leadership: Provide guidance and mentorship to junior engineers, driving best practices and contributing to the team's technical direction.

📝 Enhancement Note: This role requires a strong background in both software engineering and machine learning, with a focus on ML infrastructure and one or more specialized ML areas.

💻 Primary Responsibilities

Software Development: Write and test product or system development code, ensuring adherence to best practices and style guidelines.
Collaboration: Collaborate with peers and stakeholders through design and code reviews, driving high-quality and efficient solutions.
Documentation: Contribute to existing documentation or educational content, adapting it based on product updates and user feedback.
Issue Triage: Triage product or system issues, debugging and resolving them by analyzing the sources of issues and their impact on operations and quality.
ML Solution Design: Design and implement solutions in one or more specialized ML areas, leveraging ML infrastructure and demonstrating expertise in a chosen field.

📝 Enhancement Note: This role requires a strong focus on both software engineering and machine learning, with a particular emphasis on ML infrastructure and model deployment.

🎓 Skills & Qualifications

Education:

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

Experience:

5+ years of experience with software development in one or more programming languages, and with data structures/algorithms.
3+ years of experience testing, maintaining, or launching software products.
3+ years of experience with software design and architecture.
3+ years of experience with one or more of the following: speech/audio technology, reinforcement learning, ML infrastructure, or specialization in another ML field.
3+ years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Required Skills:

Proficiency in one or more programming languages (e.g., Python, Java, C++).
Strong understanding of data structures and algorithms.
Experience with software testing, maintenance, and launch processes.
Knowledge of ML infrastructure and model deployment, evaluation, and optimization.
Familiarity with speech/audio technology, reinforcement learning, or another ML specialization.

Preferred Skills:

Master's degree or PhD in Computer Science or a related technical field.
Experience in a technical leadership role.
Experience developing accessible technologies.

📝 Enhancement Note: This role requires a strong background in both software engineering and machine learning, with a particular emphasis on ML infrastructure and model deployment. Preferred qualifications include advanced degrees and experience in technical leadership roles.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

AI/ML Projects: Highlight AI/ML projects that demonstrate your expertise in speech/audio technology, reinforcement learning, or another ML specialization.
ML Infrastructure: Showcase your experience with ML infrastructure, including model deployment, evaluation, optimization, data processing, and debugging.
Software Development: Include software development projects that showcase your proficiency in one or more programming languages, data structures, and algorithms.
Collaboration: Demonstrate your ability to work effectively with cross-functional teams, contributing to high-quality and efficient software products.

Technical Documentation:

Code Quality: Demonstrate your commitment to code quality, commenting, and documentation standards.
Version Control: Showcase your experience with version control systems, deployment processes, and server configuration.
Testing Methodologies: Highlight your understanding of testing methodologies, performance metrics, and optimization techniques.

📝 Enhancement Note: This role requires a strong focus on both software engineering and machine learning, with a particular emphasis on ML infrastructure and model deployment. Your portfolio should demonstrate your expertise in these areas, as well as your ability to work effectively with cross-functional teams.

💵 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

Working Hours: This role operates on a standard 40-hour workweek, with flexibility for project deadlines and maintenance windows.

📝 Enhancement Note: The salary range for this role is determined by Google's compensation structure, which considers factors such as job-related skills, experience, and relevant education or training. The provided range is based on Google's disclosed information.

🎯 Team & Company Context

🏢 Company Culture

Industry: Google is a multinational technology company that specializes in internet-related services and products, including online advertising technologies, search engine, cloud computing, software, and hardware.

Company Size: Google is a large company with over 135,000 employees worldwide, providing ample opportunities for collaboration and growth.

Founded: Google was founded in 1998 by Larry Page and Sergey Brin, with a mission to "organize the world's information and make it universally accessible and useful."

Team Structure:

AI/ML Team: This role is part of the AI/ML team, focusing on developing and maintaining AI/ML models and infrastructure for Google Cloud Compute.
Cross-Functional Collaboration: Google encourages collaboration across teams, including designers, marketers, and business teams.

Development Methodology:

Agile/Scrum: Google uses Agile/Scrum methodologies for software development, with a focus on iterative development, regular feedback, and continuous improvement.
Code Review: Google emphasizes code review as a means of ensuring high-quality and efficient code, as well as knowledge sharing and collaboration.
Deployment Strategies: Google uses CI/CD pipelines and automated deployment strategies to ensure rapid and reliable software delivery.

Company Website: Google

📝 Enhancement Note: Google's company culture is characterized by its focus on innovation, collaboration, and user-centered design. This role requires strong technical skills and a commitment to driving high-quality and efficient solutions.

📈 Career & Growth Analysis

AI/ML Career Level: This role is a senior-level position within the AI/ML team, focusing on developing and maintaining AI/ML models and infrastructure for Google Cloud Compute. It requires a strong background in both software engineering and machine learning, with a particular emphasis on ML infrastructure and model deployment.

Reporting Structure: This role reports directly to the AI/ML team lead, with opportunities for technical mentorship and guidance.

Technical Impact: This role has a significant impact on Google Cloud Compute's AI/ML capabilities, driving innovation and improvement in speech/audio technology, reinforcement learning, or another ML specialization.

Growth Opportunities:

Technical Leadership: This role offers opportunities for technical leadership, mentoring junior engineers, and driving the team's technical direction.
Specialization: Google encourages specialization in AI/ML, providing opportunities for deepening expertise in speech/audio technology, reinforcement learning, or another ML specialization.
Architecture Decisions: This role offers opportunities to influence architecture decisions, driving the team's technical direction and ensuring high-quality and efficient solutions.

📝 Enhancement Note: This role offers significant opportunities for growth and development within the AI/ML team, with a focus on technical leadership, specialization, and architecture decisions.

🌐 Work Environment

Office Type: Google's offices are designed to foster collaboration and innovation, with open workspaces, meeting rooms, and recreational areas.

Office Location(s): Kirkland, WA, USA

Workspace Context:

Collaboration: Google's offices are designed to encourage collaboration, with open workspaces and meeting rooms that facilitate teamwork and knowledge sharing.
Development Tools: Google provides state-of-the-art development tools, including multiple monitors and testing devices, to ensure high-quality and efficient software development.
Cross-Functional Collaboration: Google encourages collaboration across teams, including designers, marketers, and business teams, to drive user-centered design and innovation.

Work Schedule: This role operates on a standard 40-hour workweek, with flexibility for project deadlines and maintenance windows. Google encourages work-life balance and provides resources to support employee well-being.

📝 Enhancement Note: Google's work environment is designed to foster collaboration, innovation, and user-centered design. This role requires strong technical skills and a commitment to driving high-quality and efficient solutions.

📄 Application & Technical Interview Process

Interview Process:

Technical Preparation: Prepare for technical assessments, including coding challenges and system design discussions, with a focus on AI/ML and software development fundamentals.
AI/ML Assessment: Demonstrate your expertise in speech/audio technology, reinforcement learning, or another ML specialization, with a focus on model deployment, evaluation, and optimization.
Software Development Assessment: Showcase your proficiency in one or more programming languages, data structures, and algorithms, with a focus on software testing, maintenance, and launch processes.
Final Evaluation: Demonstrate your ability to work effectively with cross-functional teams, driving high-quality and efficient software products.

Portfolio Review Tips:

AI/ML Projects: Highlight AI/ML projects that demonstrate your expertise in speech/audio technology, reinforcement learning, or another ML specialization.
ML Infrastructure: Showcase your experience with ML infrastructure, including model deployment, evaluation, optimization, data processing, and debugging.
Software Development: Include software development projects that showcase your proficiency in one or more programming languages, data structures, and algorithms.
Collaboration: Demonstrate your ability to work effectively with cross-functional teams, contributing to high-quality and efficient software products.

Technical Challenge Preparation:

AI/ML Challenges: Prepare for AI/ML challenges that focus on speech/audio technology, reinforcement learning, or another ML specialization, with a focus on model deployment, evaluation, and optimization.
Software Development Challenges: Prepare for software development challenges that focus on one or more programming languages, data structures, and algorithms, with a focus on software testing, maintenance, and launch processes.
Communication: Prepare for technical discussions and presentations, demonstrating your ability to communicate complex ideas clearly and effectively.

ATS Keywords:

AI/ML: Speech/audio technology, reinforcement learning, ML infrastructure, model deployment, model evaluation, optimization, data processing, debugging.
Software Development: Programming languages, data structures, algorithms, software testing, maintenance, launch processes, code review, best practices.
Soft Skills: Collaboration, communication, problem-solving, technical leadership, mentorship.

📝 Enhancement Note: This role requires a strong focus on both software engineering and machine learning, with a particular emphasis on ML infrastructure and model deployment. The interview process is designed to assess your technical skills and ability to work effectively with cross-functional teams.

🛠 Technology Stack & Web Infrastructure

AI/ML Technologies:

Speech/Audio: TensorFlow, PyTorch, Librosa, SpeechRecognition, etc.
Reinforcement Learning: Stable Baselines3, RLlib, RLlib, etc.
ML Infrastructure: TensorFlow Extended (TFX), MLflow, Kubeflow, etc.

Software Development Technologies:

Programming Languages: Python, Java, C++, etc.
Data Structures & Algorithms: Arrays, linked lists, trees, graphs, sorting algorithms, etc.
Software Testing: JUnit, PyTest, Mockito, etc.
Version Control: Git, SVN, etc.
Deployment: Docker, Kubernetes, Google Cloud Platform, etc.

Infrastructure Tools:

Cloud Platforms: Google Cloud Platform, Amazon Web Services, Microsoft Azure, etc.
Monitoring Tools: Prometheus, Grafana, New Relic, etc.
CI/CD Pipelines: Jenkins, GitLab CI/CD, CircleCI, etc.

📝 Enhancement Note: This role requires a strong background in both software engineering and machine learning, with a particular emphasis on ML infrastructure and model deployment. The technology stack for this role includes a wide range of AI/ML and software development technologies, as well as infrastructure tools.

👥 Team Culture & Values

AI/ML Values:

Innovation: Google values innovation and encourages its employees to think creatively and push the boundaries of what's possible in AI/ML.
User-Centered Design: Google prioritizes user-centered design, ensuring that AI/ML solutions meet the needs of users and drive meaningful impact.
Collaboration: Google values collaboration and encourages its employees to work effectively with cross-functional teams to drive high-quality and efficient solutions.
Accessibility: Google is committed to making its products and services accessible to all users, regardless of ability or circumstance.

Collaboration Style:

Cross-Functional Integration: Google encourages collaboration across teams, including designers, marketers, and business teams, to drive user-centered design and innovation.
Code Review Culture: Google emphasizes code review as a means of ensuring high-quality and efficient code, as well as knowledge sharing and collaboration.
Peer Programming: Google encourages peer programming and pair programming as means of driving knowledge sharing and collaboration.

📝 Enhancement Note: Google's AI/ML team values innovation, user-centered design, collaboration, and accessibility. This role requires strong technical skills and a commitment to driving high-quality and efficient solutions in a collaborative environment.

⚡ Challenges & Growth Opportunities

Technical Challenges:

AI/ML Challenges: This role presents technical challenges in speech/audio technology, reinforcement learning, or another ML specialization, with a focus on model deployment, evaluation, and optimization.
Software Development Challenges: This role presents technical challenges in one or more programming languages, data structures, and algorithms, with a focus on software testing, maintenance, and launch processes.
User Experience Challenges: This role presents challenges in designing and developing AI/ML solutions that meet the needs of users and drive meaningful impact.

Learning & Development Opportunities:

AI/ML Specialization: Google encourages specialization in AI/ML, providing opportunities for deepening expertise in speech/audio technology, reinforcement learning, or another ML specialization.
Technical Leadership: This role offers opportunities for technical leadership, mentoring junior engineers, and driving the team's technical direction.
Architecture Decisions: This role offers opportunities to influence architecture decisions, driving the team's technical direction and ensuring high-quality and efficient solutions.

📝 Enhancement Note: This role presents significant technical challenges in AI/ML and software development, as well as opportunities for growth and development within the AI/ML team. The learning and development opportunities for this role include AI/ML specialization, technical leadership, and architecture decisions.

💡 Interview Preparation

Technical Questions:

AI/ML Fundamentals: Prepare for questions on AI/ML fundamentals, including speech/audio technology, reinforcement learning, ML infrastructure, model deployment, evaluation, and optimization.
Software Development Fundamentals: Prepare for questions on software development fundamentals, including one or more programming languages, data structures, algorithms, software testing, maintenance, and launch processes.
System Design: Prepare for system design questions, with a focus on AI/ML and software development challenges.

Company & Culture Questions:

AI/ML Culture: Prepare for questions on Google's AI/ML culture, including innovation, user-centered design, collaboration, and accessibility.
AI/ML Methodologies: Prepare for questions on AI/ML methodologies, including Agile/Scrum, code review, and peer programming.
User Experience Impact: Prepare for questions on the user experience impact of AI/ML solutions, including project metrics and performance measurement.

Portfolio Presentation Strategy:

AI/ML Projects: Highlight AI/ML projects that demonstrate your expertise in speech/audio technology, reinforcement learning, or another ML specialization.
ML Infrastructure: Showcase your experience with ML infrastructure, including model deployment, evaluation, optimization, data processing, and debugging.
Software Development: Include software development projects that showcase your proficiency in one or more programming languages, data structures, and algorithms.
Collaboration: Demonstrate your ability to work effectively with cross-functional teams, contributing to high-quality and efficient software products.

📝 Enhancement Note: This role requires a strong focus on both software engineering and machine learning, with a particular emphasis on ML infrastructure and model deployment. The interview process is designed to assess your technical skills and ability to work effectively with cross-functional teams.

📌 Application Steps

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

Customize Your Portfolio: Tailor your AI/ML and software development projects to demonstrate your expertise in speech/audio technology, reinforcement learning, or another ML specialization, as well as your ability to work effectively with cross-functional teams.
Optimize Your Resume: Highlight your relevant AI/ML and software development skills, as well as your experience with ML infrastructure and model deployment.
Prepare for Technical Challenges: Brush up on your AI/ML and software development skills, with a focus on model deployment, evaluation, optimization, and software testing, maintenance, and launch processes.
Research Google: Familiarize yourself with Google's AI/ML culture, including innovation, user-centered design, collaboration, and accessibility, as well as its AI/ML methodologies and user experience impact.

⚠️ 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, Google Cloud Compute