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Software Engineer Infrastructure AI ML

Google

Mountain View, CAOn Site

Original Job Summary

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

Brush up on distributed system design fundamentals.
Practice ML frameworks coding challenges.
Review backend system scalability techniques.
Study system design case studies.

Behavioral Questions

Describe a challenging project you led.
Explain collaboration in cross-functional teams.
Share a time you handled ambiguous problems.
Discuss mentoring and team growth experiences.