Site Reliability Engineer, ML Compute SRE

Google • Durham, NC

5h • $141,000 - $202,000

About The Position

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

Much of our software development focuses on optimizing existing systems,
building infrastructure and eliminating work through automation. On the SRE
team, you’ll have the opportunity to manage the complex challenges of scale
which are unique to Google Cloud, while using your expertise in coding,
algorithms, complexity analysis and large-scale system design. SRE's culture of
intellectual curiosity, problem solving and openness is key to its success. Our
organization brings together people with a wide variety of backgrounds,
experiences and perspectives. We encourage them to collaborate, think big and
take risks in a blame-free environment. We promote self-direction to work on
meaningful projects, while we also strive to create an environment that provides
the support and mentorship needed to learn and grow.

The ML Accelerator SRE team mission is to deliver an exceptional ML compute
infrastructure for all users. We ensure that all ML accelerators are fully and
appropriately supported as part of the TI and Cloud Compute platforms and that
all ML jobs run efficiently, safely and reliably. We support both the hardware
and the low level services that provide ML as an IaaS.

Requirements

• Bachelor’s degree in Computer Science, a related field, or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages (e.g., Golang).
• Experience with debugging and troubleshooting software issues.

Nice To Haves

• Master's degree in Computer Science or Engineering, a related field, or equivalent practical experience.
• 4 years of experience designing, analyzing, and troubleshooting large-scale distributed systems.
• 2 years of experience with data structures and algorithms.
• Experience with Machine Learning infrastructure.
• Experience in statistical analysis to identify trends and root causes in production data.

Responsibilities

• Design/develop new features to help us support ML operations across Technical Infrastructure (TI) and Google Cloud Platform (GCP).
• Participate in tier 2 on-call support for ML accelerator and platform issues.
• Identify and improve our operational experience and help us reduce toil and reduce incident impact.
• Define and improve metrics and Service Level Objectives (SLOs) for the operations of the ML fleet.
• Work with Platform Infrastructure Engineering (PIE), Cloud, and SRE stakeholders to understand and reduce risks to upcoming accelerator launches.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level