info_outline
X Note: By applying to this position you will have an opportunity to share your preferred working location from the following:
San Bruno, CA, USA; Mountain View, CA, USA.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 5 years of experience with 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 with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
- 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.
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 jobAs a Senior Software Engineer for ML Infrastructure at YouTube, you will directly envision, design, and lead implementation of new state-of-the-art capabilities in our JAX-based next-generation ML frameworks tailored to YouTube's recommender model training needs.
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 - 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).
- Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
- Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.
- Write and test product or system development code.
