Senior Software Engineer, AI/ML, Google Workspace

Google•Sunnyvale, CA

3h•$174,000 - $252,000

About The Position

Google software engineers develop next-generation technologies that change how billions of users connect, explore, and interact with information. Engineers work on projects critical to Google’s needs and may have opportunities to switch teams and projects as the business evolves. The role calls for versatility, leadership qualities, and enthusiasm for tackling problems across the full stack. This position focuses on AI for Google Workspace products, including Gmail, Docs, Drive, Calendar, Sheets, Vids, and Meet. The team aims to shape the future of productivity tools, including capabilities such as email and meeting summaries and videos created from documents using AI avatars. The engineer will work with model builders at Google DeepMind and contribute to products used by people around the world. Individual pay is determined by job-related skills, experience, and relevant education or training. The listed U.S. compensation is $174,000–$252,000, plus a 15% bonus target, equity, and benefits.

Requirements

Bachelor’s degree or equivalent practical experience.
5 years of experience programming in Python or C++.
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
3 years of experience in one or more of the following: speech/audio, reinforcement learning, machine learning infrastructure, or specialization in another machine learning field.
3 years of experience with end-to-end machine learning, such as model deployment, model evaluation, optimization, data processing, and debugging.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
5 years of experience with data structures and algorithms.
1 year of experience in a technical leadership role.
Experience developing accessible technologies.

Responsibilities

Write and test product or system development code.
Collaborate with peers and stakeholders through design and code reviews to ensure best practices for available technologies, including style guidelines, code checking, 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.
Design and implement solutions in one or more specialized machine learning areas, leverage machine learning infrastructure, and demonstrate expertise in a chosen field.