Software Engineer III, AI/ML, Ads Quality

Google•Mountain View, CA

1d•$147,000 - $210,000

About The Position

Google software engineers develop technologies used by billions of people and work on projects critical to Google’s needs. Engineers are expected to bring ideas from across technology, demonstrate versatility and leadership, and take on new problems across the full stack as the business evolves. This role is part of Google Ads, which applies machine learning and generative AI models, including Gemini, to support its global advertising business. The work aims to protect users from harm, improve ad quality, and optimize campaigns for advertiser return on investment. The role involves implementing solutions in specialized machine-learning areas, using ML infrastructure, and contributing to model optimization and data processing. The team collaborates with peers, stakeholders, Google Research, and DeepMind on complex challenges at large scale.

Requirements

Bachelor’s degree or equivalent practical experience.
At least 2 years of software development experience, such as in C++ or Python, and experience with data structures and algorithms.
At least 1 year of end-to-end machine-learning experience, such as model deployment, model evaluation, data processing, or debugging.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
Experience with analytic methodologies such as regression, classification, prediction, machine learning, and neural networks.

Responsibilities

Write product or system development code.
Collaborate with peers and stakeholders through design and code reviews to promote best practices, 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 impact on hardware, network, or service operations and quality.
Implement solutions in one or more specialized machine-learning areas, use ML infrastructure, and contribute to model optimization and data processing.