Engineering Director, Spatial Flex
- Location
- Sunnyvale, CA, USA; Kirkland, WA, USA
- Work model
- On-Site
- Level
- Staff
- Salary
- $307k – $427k/yr
- H-1B history
- 2,460 approvals (FY2023)
- Posted
- 2h ago
Skills
About this role
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. Google is undergoing the largest transformation of its technical infrastructure in history, driven by the explosive growth of our AI and Machine Learning (ML) platforms and the increasing size and scale of our data centers worldwide. This pivotal role is at the center of this change, responsible for successfully shaping the infrastructure's future to meet next-generation demands. The Spatial Flexibility team builds the core systems that efficiently manage all of Google's compute, storage, and AI/ML resources globally. Our mission is to unlock the highest lifetime efficiency and business utility of these planet-scale resources by fundamentally transforming how global workload footprint is managed. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $307000 - $427000 (USD) + 30% bonus target + equity + benefits Learn more about benefits at Google .
Provide technical vision and strategy. Build a multi-year technical roadmap, balancing short- and long-term technology investments in a commercial, mission-critical environment. Partner closely and influence product management, product engineering, and other Google engineering teams to improve speed, quality, and ease. Build and lead an engineering team to innovate, invent, implement, and deploy complex software solutions. Unlock roadblocks at corporate level and cultivate collaboration with senior technologists across Google through leadership, creativity, intelligence, and presence. Develop and grow talent through effective mentoring, coaching, succession planning, and retention strategies for key talent. Attract great talent internally and externally.
Minimum qualifications: Bachelor's degree in Computer Science or equivalent practical experience. 15 years of leadership experience. Experience in creating roadmaps balancing engineering resources and business goals. Experience as a technical leader in influencing multiple teams of engineers concurrently while partnering on development initiatives. Preferred qualifications: Experience in building and running cloud infrastructure (private or public cloud) or large-scale service systems that are highly available and reliable. Familiarity with AI/ML concepts, especially in areas such as Machine Learning, Inference, Performance Optimization, Capacity Management, and Inference Efficiency and Optimizations. Ability to learn AI specifics.