SRE Systems Engineering Manager, ML Compute

Google Inc.; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Google is hiring a Systems Engineering Manager for Site Reliability Engineering focusing on ML Compute in London. You will lead a team responsible for uptime, availability and performance of core services and drive automation across large-scale infrastructure.

Own end-to-end reliability, mentor engineers, manage on-call rotations across continents, and collaborate with product and platform teams to deliver scalable, fault-tolerant ML compute environments.

Qualifications

• Bachelor's degree in Computer Science or a related technical field or equivalent practical experience.
• 5 years of experience with programming in one or more programming languages.
• 3 years of people management experience.
• 3 years of experience leading projects and working with administration (e.g., filesystems, inodes, system calls) or networking (e.g., TCP/IP, routing, network topologies and hardware, SDN).

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.

Skills

Programming experience

People management

Project leadership

Networking basics

Decision making

Problem solving

Education

Bachelor's degree in Computer Science or related field

Job description

Google is hiring a Systems Engineering Manager for Site Reliability Engineering focusing on ML Compute in London. You will lead a team responsible for uptime, availability and performance of core services and drive automation across large-scale infrastructure.

Own end-to-end reliability, mentor engineers, manage on-call rotations across continents, and collaborate with product and platform teams to deliver scalable, fault-tolerant ML compute environments.