Senior Software Engineer, AI/ML, Google Ads

Google•Mountain View, CA

3d•$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, with opportunities to switch teams and projects as the business evolves. The role calls for versatility, leadership qualities, and enthusiasm for taking on new problems across the full stack. Google Ads applies machine learning and generative AI models, including Gemini, to support a global business. The work affects billions of users through efforts to protect users from harm, improve ad quality, and optimize advertiser campaign return on investment. The team collaborates closely with groups such as Google Research and DeepMind to solve complex challenges and develop advertising technology at scale. Individual pay is determined by job-related skills, experience, and relevant education or training. The listed US compensation is $174,000–$252,000 USD, 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, 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 checking, accuracy, testability, and efficiency.
Contribute to existing documentation or educational content and adapt content based on product or program updates and user feedback.
Triage product or system issues and debug, track, and resolve them by analyzing issue sources and impacts on hardware, network, or service operations and quality.