Senior Software Engineering Manager, Site Reliability Engineering, YouTube ML

Site Reliability Engineering (SRE) combines software and systems engineering to build and run large-scale, massively distributed, fault-tolerant systems. SRE ensures that Google's services—both our internally critical and our externally-visible systems—have reliability, uptime appropriate to users' needs and a fast rate of improvement. Additionally SRE’s will keep an ever-watchful eye on our systems capacity and performance.

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.

Responsibilities

• Lead a team of software/systems engineers on projects for users and be directly responsible for uptime.
• Own end-to-end availability and performance of key services and build automation to prevent problem recurrence. Automate response to all non-exceptional service conditions.
• Lead by example, mentor the team and establish credibility through quality technical execution.
• Manage on-call rotations across continents, using a follow-the-sun model.
• Design, write and deliver software to improve the availability, scalability, latency and efficiency of Google's services.

Minimum qualifications:

• Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
  
• 8 years of experience with software development in one or more programming languages.
  
• 5 years of experience managing people.
  
• 3 years of experience in designing, analyzing, and troubleshooting distributed systems.

Preferred qualifications:

• Master's degree in Computer Science or Engineering.
  
• Experience with machine learning infrastructure.

In most instances, this position requires in-person interviews as part of the hiring process.