Software Engineer, Infrastructure, Google Wide Profiling

Software Engineer, Infrastructure, Google Wide Profiling

Google place Sunnyvale, CA, USA

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• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree in an industry setting.
• 1 year of experience with distributed computing, big data analytics, cloud computing, multi-tenant cloud, or AI.

• Master's degree or PhD in Computer Science or a related technical field.
• 2 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
• Experience coding in C++, Java, Python or Go.

Google-Wide Profiling (GWP) is one of the key tools we build that plays a big role in saving CPU and RAM resources across the Google fleet. GWP covers a broad spectrum of Google production technologies through its collection, processing, analysis, and visualization of high-volume performance data. As the Google fleet changes, GWP has to evolve with it. In particular, Google is expanding its support for Arm-based computing and other kinds of hardware platforms.

This brings new tests around understanding shifts in the nature of compute work done itionally, GWP needs to scale to serve as a warehouse of performance data going back over 10 years. To support Google’s growing investments in AI and Cloud, delivering tools that enable optimization of the standard production fleet will be critical to Google’s long term success.

In this role, you will design features, develop code in Go and C++, and collaborate with teammates and partners to develop tooling that enables greater efficiency across Google’s production fleet.

The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Google-Wide Profiling (GWP) is one of the key tools we build that plays a big role in saving CPU and RAM resources across the Google fleet. GWP covers a broad spectrum of Google production technologies through its collection, processing, analysis, and visualization of high-volume performance data. As the Google fleet changes, GWP has to evolve with it. In particular, Google is expanding its support for Arm-based computing and other kinds of hardware platforms.

This brings new tests around understanding shifts in the nature of compute work done itionally, GWP needs to scale to serve as a warehouse of performance data going back over 10 years. To support Google’s growing investments in AI and Cloud, delivering tools that enable optimization of the standard production fleet will be critical to Google’s long term success.

In this role, you will design features, develop code in Go and C++, and collaborate with teammates and partners to develop tooling that enables greater efficiency across Google’s production fleet.

The US base salary range for this full-time position is $141,000-$202,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

• Redesign the GWP’s collection and processing infrastructure to further scale with the fleet and enable better cross-architecture analysis.
• Improve GWP query speed through the use of pre-aggregation, new querying technology and analysis of usage patterns.
• Optimize the…