Senior Software Engineer, AI/ML, Google Cloud Platforms

Google•Kirkland, WA

1d•$174,000 - $252,000

About The Position

Google’s software engineers develop next-generation technologies used by billions of people to 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. Within Google Cloud, the position contributes to enterprise-grade solutions that help organizations digitally transform their businesses and industries. The engineer designs and implements solutions in specialized machine learning areas, uses ML infrastructure, and demonstrates expertise in a chosen field. Individual pay is determined by job-related skills, experience, and relevant education or training.

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, such as model deployment, model evaluation, 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 across available technologies, including style guidelines, code check-in, 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 impacts on hardware, network, or service operations and quality.
Design and implement solutions in one or more specialized machine learning areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.