Senior Software Engineer, YouTube Ads Marketplace Optimization
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience with end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging).
- Experience designing and operating distributed computing systems for large-scale AI/ML workloads.
- Experience applying Machine Learning techniques to build predictive models.
- Experience in building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- 1 year of experience in a technical leadership role.
- Experience with graph algorithms and graph mining.
- Experience applying analytical reasoning to software engineering problems.
- Experience developing accessible technologies.
- Ads experimentation and analysis.
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.
Grow the YouTube Ads business by understanding user interest and recommend ads that are aligned with user interest. 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, rich user generation, and knowledge distillation for training more compact models.
Work with a team of software engineers, ML engineers, and Data Scientists to understand long-term user interest from a source of information from across the company. Build a large recommendation system using LLMs, Sequence Modeling, GNNs, and other similar algorithms. Predict likelihood of user interest in a particular ad.
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
- Apply AI, machine learning, and LLMs to optimize the YouTube Ads marketplace.
- Develop sequence and graph-based AI models to improve ads optimization.
- Develop distributed data processing pipelines for large-scale ads data.
- Develop and connect various serving components within the ad stack.
- Analyze and optimize the YouTube ads ecosystem and serving stack. Sequence and Graph based AI models by developing distributed data processing pipelines, develop and connect various serving components.
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