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 building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Experience with building Deep Learning models with the core TensorFlow, PyTorch, or JAX .
- Experience building architecture.
Preferred qualifications:- Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures and algorithms.
- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.
About the jobManage end-to-end modeling for the whole personalization flow including retrieval, ranking, user understanding, and prediction for search notification. Work on state of the art modeling problems including sequence-based user understanding, real-time training, personalization with LLM empowerment, etc.
In Google Search, we're reimagining what it means to search for information - any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Write and test product or system development code.
- Innovate of adapting recommendation algorithms towards new applications and new surfaces.
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Experiment with novel signals, labels, architectures, policies and training regimens.
- Design and implement recommendation systems models across different domains, leverage ML infrastructure, and contribute to architecture design.
