The role
From Google's own posting.
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.
In this role, you will help lead a team of ML/AI/SW engineers to grow the YouTube Ads business by understanding user interest and recommend ads that are aligned with user interest (i.e., YT's ad user models). The YouTube Ads User Models are a suite of large-scale AI systems designed to model users' commercial interests by leveraging Google-wide data sources approved for advertising purposes. Designed for multi-task learning across various surfaces, these models are applied to numerous downstream tasks, including retrieval, user action prediction, and knowledge distillation for training more compact models.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google .
Design, develop, test, deploy, maintain, and enhance large scale software solutions.
Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
Lead the design and implementation of recommendation systems, optimize ML infrastructure, and guide the development of model architecture.
Minimum qualifications:
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience integrating generative AI tools or LLM interfaces into workflows.
Preferred qualifications:
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
8 years of experience with data structures and algorithms.
3 years of experience in a technical leadership role leading project teams and setting technical direction.
3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience integrating generative AI tools or LLM interfaces into workflows.