Staff Software Engineer, Performance, Segment Optimized Computing

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development, and with data structures/algorithms.
• 5 years of experience with design and architecture; and testing/launching software products.

Preferred qualifications:

• Master's degree or PhD in Computer Engineering, Electrical Engineering, or Computer Science.
• Experience with computer architecture, performance analysis, and performance modeling.
• Experience with large-scale distributed systems, networking, databases, queueing theory, and related software infrastructure.
• Experience with OS and storage systems, with the ability to root cause performance bottlenecks from an end-to-end perspective.
• Experience in people management.
• Driven and good problem-solving skills and ability to work across different teams across the hardware/software stack.

About the job

Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Are you uncomfortably excited about solving big Systems Architecture challenges? If yes, come join this effort and help shape the future of computing at Google and in the industry.

To meet Google's increasing ML and Cloud demands with sustainable cost, we need to identify and implement novel solutions to flatten the compute and memory spend curves, which requires a new suite of analytics, models, tools, and systems for exploring and evaluating effective ideas.

You will be working on new server platforms and hardware-software codesign to power Google internal and Cloud workloads. These will include providing performance evaluation/analysis/optimization support for new hardware products, evaluating and developing new system architectures, as well as improving the end-to-end software stack.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.

Responsibilities

• Perform performance/TCO evaluation, analysis, and optimization for new server platforms. Conduct end-to-end performance analysis for deployed platforms to understand and improve performance bottlenecks.
• Characterize end-to-end system performance from application to infrastructure to hardware.
• Model and analyze key Google internal and Cloud workloads to drive server platform architecture design.
• Develop systems tailored towards Google requirements. Build infrastructure and tools to help engineers easily identify performance bottlenecks at the hardware and system software level.
• Manage a team of 5-6 engineers.