Software Engineer, AI/ML, Ads Quality

Google•New York, NY

4d•$147,000 - $210,000

About The Position

Google software engineers develop technologies used by billions of people across products and systems that operate at massive scale. In this role, you will work on a project important to Google, with opportunities to move between teams and projects as the business evolves. The position calls for versatility, leadership qualities, and enthusiasm for solving problems across the full stack. Within Google Ads, the team applies machine learning and Generative AI models, including Gemini, to support a global advertising business. The work aims to protect users from harm, improve ad quality, and optimize campaigns for advertiser return on investment. The role offers collaboration with teams including Google Research and DeepMind on large-scale challenges in advertising technology. Individual pay is determined by job-related skills, experience, and relevant education or training. The stated U.S. compensation is $147,000–$210,000, plus a 15% bonus target, equity, and benefits.

Requirements

Bachelor’s degree or equivalent practical experience.
At least 2 years of software development experience in one or more programming languages, or 1 year of experience with an advanced degree.
At least 2 years of software development experience with C++.
At least 1 year of end-to-end machine learning experience, such as model deployment, model evaluation, data processing, or debugging.
At least 1 year of quantitative data analysis experience.

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.
Experience with auction theory and related disciplines, such as game theory.

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, code submission, 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 root causes and their impact on hardware, network, or service operations and quality.
Implement solutions in one or more specialized machine learning areas, use machine learning infrastructure, and contribute to model optimization and data processing.