Senior Software Engineer, AI/ML Recommendations, Rankings, Predictions, YouTube

Google•Mountain View, CA

3d•$174,000 - $252,000

About The Position

Google is seeking a Senior Software Engineer for YouTube to develop recommendation systems involving rankings, predictions, retrieval, personalization, search quality, and embeddings. The role contributes to product or system development at Google scale, using machine learning infrastructure and participating in architecture design. Engineers work with peers and stakeholders and may work on projects across the full stack as business needs evolve. The position is at the intersection of technology and YouTube’s efforts to help people share stories, explore interests, and connect.

Requirements

Bachelor’s degree or equivalent practical experience.
5 years of experience programming in Python and 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 building and deploying recommendation systems models in production, including retrieval, prediction, ranking, personalization, search quality, or embeddings, and experience building architecture.
3 years of experience with end-to-end machine learning, such as model deployment, model evaluation, data processing, and debugging.

Nice To Haves

Master’s degree or PhD in Computer Science or a 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.

Responsibilities

Write and test product or system development code.
Collaborate with peers and stakeholders through design and code reviews to promote best practices, including style guidelines, code checking, accuracy, testability, and efficiency.
Contribute to existing documentation or educational content and adapt it based on product or program updates and user feedback.
Triage product or system issues and debug, track, and resolve them by analyzing causes and impacts on hardware, network, service operations, and quality.
Design and implement recommendation systems models across different domains, leverage machine learning infrastructure, and contribute to architecture design.