Software Engineer III, AI/ML, Search

Google•Mountain View, 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. The role involves work on a project critical to Google, with opportunities to move between teams and projects as the business evolves. Engineers are expected to be versatile, demonstrate leadership qualities, and take on problems across the full stack. On the Google Search team, you will help reimagine how people search for information, address complex engineering challenges, and expand infrastructure while maintaining an accessible and useful experience for people around the world. The position focuses on implementing solutions in specialized machine learning areas and using ML infrastructure, including model optimization and data processing.

Requirements

Bachelor’s degree or equivalent practical experience.
At least 2 years of software development experience in one or more programming languages, or at least 1 year of experience with an advanced degree.
At least 1 year of experience in one or more of the following: speech/audio, reinforcement learning, ML infrastructure, or another ML specialization.
At least 1 year of experience with ML 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.
At least 2 years of experience with data structures and algorithms.
Experience developing accessible technologies.

Responsibilities

Write 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, checking in code, 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 causes and impacts on hardware, network, or service operations and quality.
Implement solutions in one or more specialized ML areas, use ML infrastructure, and contribute to model optimization and data processing.