Senior Software Engineer, AI/ML, Platforms and Devices

Google•Mountain View, CA

1d•$174,000 - $252,000

About The Position

Google software engineers develop technologies used by billions of people to connect, explore, and interact with information. Engineers work on projects critical to Google, with opportunities to move between teams and projects as business needs evolve. The role calls for versatility, leadership, and enthusiasm for tackling problems across the full stack. The Platforms and Devices team works across Google's computing software platforms—including desktop, mobile, and applications—and its first-party devices and services. Teams research, design, and develop technologies that make user interactions with computing faster and more seamless, creating experiences for users around the world. This role focuses on software engineering in AI/ML and platforms and devices. It includes developing and testing product or system code, designing and implementing solutions in specialized ML areas, using ML infrastructure, collaborating in design and code reviews, maintaining documentation, and resolving product or system issues.

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 with one or more of the following: speech/audio, reinforcement learning, ML infrastructure, or specialization in another ML field.
3 years of experience with end-to-end Machine Learning infrastructure, such as model deployment, model evaluation, optimization, data processing, or 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 across available technologies, including style guidelines, code check-in, accuracy, testability, and efficiency.
Contribute to 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 ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.