Computer Science Google Jobs in Austin, TX (NOW HIRING)

Minimum qualifications:

• Bachelor's degree, or equivalent practical experience.
• 8 years of experience in software development.
• 3 years of experience in a technical leadership role.
• 2 years of experience in a people management or team leadership role.
• Experience in test automation and failure analysis.
• Experience in embedded systems and software development.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 3 years of experience working in a complex, matrixed organization.
• Experience in technical troubleshooting.

About the job
Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.
With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.
The Platforms Infrastructure Engineering (PIE) Core Systems and Capacity Assurance team in Platforms Infrastructure Engineering is responsible for delivering the tools and systems that manage the state of a machine throughout it's service lifetime while part of the Google Fleet. We are also responsible for the diagnostic tests, and repair strategies to ensure that a machine is healthy and ready to serve. This includes software installation, hardware health testing, and machine management.
The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities

• Set and communicate team priorities that support the broader organization's goals. Align strategy, processes, and decision-making across teams.
• Set clear expectations with individuals based on their level and role and aligned to the broader organization's goals. Meet regularly with individuals to discuss performance and development and provide feedback and coaching.
• Develop the mid-term technical goals and roadmap within the scope of your (often multiple) team(s). Evolve the roadmap to meet anticipated future requirements and infrastructure needs.
• Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.
• Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).

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