Senior Software Engineer, AI/ML, YouTube Ads

Company : Google
Location : Mountain View, CA, 94043
Posted Date : 10 October 2025
Job Type : Full Time
Category : Software Development
Occupation : Software Engineer
Job Details
Senior Software Engineer, AI/ML, YouTube Ads
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.
Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We're made up of multiple teams, building Google's Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Minimum Qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience with software development in C++ and Python, and with data structures/algorithms.
- 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 one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
Preferred Qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.
- Experience with external ML tools (e.g., PyTorch, ML Flow, WandB), or Google ML tools (e.g.,TensorFlow, TPUs).
- Knowledge of statistical modeling and quantitative analysis techniques.
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).
- Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure.
- Build machine learning systems on data, and build knowledge on Machine learning (ML) techniques for recommendation systems, ads, and product landscape.
- Manage data analysis both offline on model prediction accuracy, and online on live experiments on business critical metrics.
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