Software Engineer III, AI/ML, Google Cloud Platforms

Google•Sunnyvale, CA

2d•$147,000 - $210,000

About The Position

Google software engineers develop technologies that help billions of users connect, explore, and interact with information. Engineers work on projects critical to Google’s needs and may move between teams and projects as the business evolves. The role calls for versatility, leadership qualities, and enthusiasm for solving problems across the full stack. This position is part of the AI and Infrastructure team, which delivers AI and infrastructure capabilities at scale, efficiency, reliability, and velocity for Google employees, Google Cloud customers, and Google users worldwide. The team supports AI model development, computing for global services, and platforms for developers, with work spanning software and hardware, including TPUs, Vertex AI for Google Cloud, global networking, data center operations, and systems research. Individual pay is determined by job-related skills, experience, and relevant education or training. The listed U.S. compensation is $147,000–$210,000 USD, plus a 15% bonus target, equity, and benefits.

Requirements

Bachelor’s degree or equivalent practical experience.
2 years of experience coding in Golang.
2 years of experience coding in Python.
1 year of experience in one or more of the following: speech/audio, reinforcement learning, machine learning infrastructure, or another specialized machine learning field.
1 year of experience with end-to-end machine learning, such as model deployment, model evaluation, data processing, or debugging.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
2 years of experience with data structures and algorithms.
2 years of experience with Generative AI.
2 years of experience with Large Language Models.
Experience with server platforms.

Responsibilities

Write product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices in areas such as style guidelines, code submission, accuracy, testability, and efficiency.
Contribute to existing documentation or educational content and adapt it based on product or program updates and user feedback.
Triage product or system issues and debug, track, and resolve them by analyzing their sources and their impact on hardware, network, or service operations and quality.
Implement solutions in one or more specialized machine learning areas, use machine learning infrastructure, and contribute to model optimization and data processing.