Software Engineer III, AI/ML, Search Ads

Google•Mountain View, CA

1d•$147,000 - $210,000

About The Position

Google software engineers develop technologies that enable users to connect, explore, and interact with information at massive scale. 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, and enthusiasm for solving problems across the full stack. Google Ads applies machine learning and Generative AI models, including Gemini, to support a global business. This work aims to protect users from harm, improve ad quality, and optimize advertiser campaign return on investment. The role involves collaborating with teams such as Google Research and DeepMind to address complex challenges and build advertising technology at scale. Individual pay is determined by job-related skills, experience, and relevant education or training. The listed 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 C++, Java, and using LLM coding tools.
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 experience with one or more of the following: Generative AI/LLM tools, reinforcement learning (such as sequential decision making), ML infrastructure, or specialization in another ML field.

Nice To Haves

Master’s degree or PhD in Computer Science or a related technical field.
Experience with the latest AI/ML research and trends.
Experience designing and building scalable full-stack systems and self-service platforms.
Knowledge of the search or Ads stack.
Excellent analytical thinking and data analysis skills.

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 submission, accuracy, testability, and efficiency.
Contribute to 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, 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.