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Form of workMinimum qualifications:
- 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.
- 2 years of experience with data structures or algorithms.
Preferred qualifications:
- Experience designing and implementing a complex or experimental system in a research or product development environment.
- Experience with storage systems, memory systems, network systems, novel I/O systems or platform architectures, including persistent memory (e.g., NVRAM), accelerators, and other emerging technologies.
- Experience with computer systems and intersection of ML (e.g., ML for systems or systems for ML).
- Ability to recognize research opportunities and define and carry out a research agenda, from problem definition to final results.
- Ability to provide analysis of design alternatives and trade-offs, often combining aspects from multiple technical fields and subfields.
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.
Systems Research Group (SRG) is positioned in the heart of Google’s Cloud and Infrastructure engineering organization, with the mission of shaping the future of hyperscaler systems design for Google and its ecosystem. It is focused on inventing, incubating, and infusing new concepts, designs, and technologies into Google’s applications, systems, and data centers. The team’s position will allow seamless engagement with engineering and product teams, enabling joint exploration in concert with transformative workloads. Beyond Google, the SRG team will look to forge relationships with external research communities working on the most pressing systems-research problems.
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 $133,000-$194,000 + 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 for new hire 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
- Contribute for the research and development team exploring emerging technologies and systems.
- Participate in team projects to carry out system design, analysis, and building of prototypes to demonstrate advantages.
- Communicate technology issues for a broad range of applications in the data center and modern Cloud environments, including machine learning.
