Senior Software Engineer, AI/ML GenAI, Google Ads

Google•Mountain View, CA

7h•$174,000 - $252,000

About The Position

Google software engineers develop next-generation technologies that enable billions of users to connect, explore, and interact with information. They work on projects critical to Google’s needs, with opportunities to move between teams and projects as the business evolves. Engineers are expected to be versatile, demonstrate leadership qualities, and take on new problems across the full stack. This role is within Google Ads, which applies machine learning and Generative AI models such as Gemini to support a global advertising business. The work focuses on protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return on investment. The role involves close collaboration with peers, stakeholders, Google Research, and DeepMind to develop AI-powered advertising technology at scale. Individual pay is determined by job-related skills, experience, and relevant education or training. The listed U.S. 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 with end-to-end machine learning, such as model deployment, model evaluation, data processing, or debugging.
1 year of experience with state-of-the-art Generative AI techniques, such as LLMs, multimodal systems, or large vision models, or with related concepts such as language modeling or computer vision.

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 promote best practices, including style guidelines, code check-in, 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 root causes and impacts on hardware, network, service operations, and quality.
Design and implement Generative AI solutions, leverage machine learning infrastructure, and evaluate tradeoffs between techniques and their application domains.