Software Engineer Infrastructure AI ML @ Google
placeMountain View, CA
attach_money $200,000
businessOn Site
scheduleFull Time
Posted 15 hours ago
Your Application Journey
Interview
Email Hiring Manager
***** @google.com
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Job Details
Overview
Google is seeking a Software Engineer Infrastructure AI ML to design, build, and maintain scalable machine learning infrastructure for Chrome. This role is critical to developing next-generation technologies that impact billions of users through on-device and server-side solutions.
Minimum Qualifications
Bachelor’s degree or equivalent experience, 5 years in software development, 3 years in testing/maintenance/launching software products, 1 year in software design and architecture, with experience in distributed systems or back-end infrastructure.
Preferred Qualifications
- Experience with Machine Learning concepts, tools, and frameworks (e.g., TensorFlow, TFLite).
- ML infrastructure experience including model serving and data pipelines.
- Familiarity with codebases like Chromium.
- Expertise in ML performance, debugging, and large-scale systems data analysis.
- Experience in leading technical projects and on-device ML deployment.
Responsibilities
- Design, build, maintain, and optimize scalable ML infrastructure for Chrome features.
- Collaborate with Chrome feature teams integrating ML solutions.
- Lead technical design and implementation of complex, multi-quarter projects.
- Mentor team engineers and support technical growth.
- Contribute to code health, system maintainability, and documentation.
Compensation & Benefits
The base salary range is $166,000-$244,000 plus bonus, equity, and benefits. Individual pay is determined by location, skills, and experience.
Key skills/competency
- Software Development
- Distributed Systems
- Machine Learning
- Infrastructure
- Scalability
- Backend
- Technical Leadership
- Mentoring
- System Design
- Optimization
How to Get Hired at Google
🎯 Tips for Getting Hired
- Customize your resume: Tailor it with relevant ML infrastructure experience.
- Highlight technical projects: Demonstrate complex system design skills.
- Research Google: Understand their products and culture deeply.
- Prepare examples: Bring clear examples of distributed systems work.
📝 Interview Preparation Advice
Technical Preparation
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Brush up on distributed system design fundamentals.
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Practice ML frameworks coding challenges.
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Review backend system scalability techniques.
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Study system design case studies.
Behavioral Questions
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Describe a challenging project you led.
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Explain collaboration in cross-functional teams.
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Share a time you handled ambiguous problems.
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Discuss mentoring and team growth experiences.
Frequently Asked Questions
What background qualifies for a Software Engineer Infrastructure AI ML role at Google?
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How important is ML experience for Google’s Software Engineer Infrastructure AI ML position?
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What programming languages are essential for this role at Google?
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How does mentoring factor into the Software Engineer Infrastructure AI ML role at Google?
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What does the compensation structure look like for this position at Google?
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