Senior Software Engineer, AI/ML, Ads Bidding.

Minimum qualifications:

• 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 (e.g., model deployment, model evaluation, optimization, debugging).
• 3 years of experience with one or more of the following: LLMs, Multi-Modality, Large Vision Models, or reinforcement learning (e.g., sequential decision making).

Preferred qualifications:

• Master's degree or PhD in a quantitative field such as Statistics, Engineering, or Mathematics.
• 5 years of experience with data structures/algorithms.
• 1 year of experience in a technical leadership role.
• Experience developing accessible technologies.

About the job:

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

We build and maintain machine learning models using state-of-the-art AI and ML techniques to predict user interactions on Search Ads. These models are a key component in setting advertisers' bids, with the goal of improving both satisfaction and return on investment for Search Ads advertisers using Auto-bidding products. By optimizing towards advertisers' objectives, Auto-bidding products drive Google's global Ads business.

You will be involved in the full machine learning model life-cycle, from design and training to deployment and serving models in production at the scale of billions of Search Ads. This role offers the opportunity to innovate while collaborating with other teams, including research teams, to test and implement the latest technologies in our models.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Write and test product or system development code. 
• Collaborate with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Solve complex machine learning related problems by designing, running, and analyzing experiments using analytical and statistical methods.
• Innovate and iterate on machine learning model design, improving quality, stability, and efficiency across the entire model life-cycle—from concept to deployment.