Senior Software Engineer, AI/ML, Ads Bidding

Google•New York, NY

3h•$174,000 - $252,000

About The Position

Google software engineers develop technologies that help billions of users connect, explore, and interact with information. This role focuses on machine learning models that predict user interactions on Search Ads. The models help set advertisers’ bids and improve satisfaction and return on investment for advertisers using Auto-bidding products. The engineer will work across the machine learning model life cycle, from design and training through deployment and production serving at the scale of billions of Search Ads. The role includes opportunities to innovate and collaborate with other teams, including research teams, to test and implement new technologies. Google Ads applies machine learning and Generative AI models, including Gemini, to its global advertising business.

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.
1 year of experience with software design and architecture.
3 years of experience with end-to-end machine learning, such as model deployment, evaluation, optimization, or debugging.
3 years of experience with one or more of the following: LLMs, multimodality, large vision models, or reinforcement learning, such as sequential decision making.

Nice To Haves

Master’s degree or PhD in a quantitative field such as Statistics, Engineering, or Mathematics.
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 in areas such as style guidelines, code review, accuracy, testability, and efficiency.
Contribute to documentation or educational content and adapt it based on product or program updates and user feedback.
Solve complex machine learning problems by designing, running, and analyzing experiments using analytical and statistical methods.
Innovate and iterate on machine learning model design to improve quality, stability, and efficiency across the model life cycle, from concept to deployment.