Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience building and developing large-scale data infrastructure, distributed systems, or large-scale libraries utilizing C .
- 5 years of experience testing, and launching software products.
- 3 years of experience with software design and architecture.
- 3 years of experience with ML model architecture and training.
Preferred qualifications:- Master's degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures/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.
- Excellent communication skills.
About the jobAs a Staff Software Engineer for ML Data Infrastructure at YouTube, you will be responsible for designing, developing, and maintaining large-scale data processing pipelines in C to support next-generation machine learning models and training methodologies. In this technical leadership role, you will propose initiatives to enhance infrastructure, secure alignment and commitment from internal clients, and architect novel solutions for evolving ML data use cases. Additionally, you will collaborate with cross-functional engineering teams focused on recommendation quality, storage systems, data logging, and privacy compliance.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Provide technical leadership on high-impact projects.
- Influence and coach a distributed team of engineers.
- Facilitate alignment and clarity across teams on goals, outcomes, and timelines.
- Manage project priorities, deadlines, and deliverables.
- Design, develop, test, deploy, maintain, and enhance large-scale software solutions.
