Minimum qualifications:
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 8 years of experience with distributed compute environments, software development, big data analytics, cloud computing including virtualization, hosted services, multi tenant cloud infrastructures, storage systems or content delivery networks.
- 3 years of technical leadership and people management experience.
Preferred qualifications:
- 10 years of experience in distributed systems, storage systems, or databases.
- Experience with artificial intelligence, machine learning, or other transformative technologies.
- Experience in architecting cloud-based analytics systems and dashboarding experiences.
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.
Our mission is to develop, generalize, and scale applied AI techniques to improve large and critical systems at Google.
The AI for Large Scale Systems (AILS) team aims to transform the design, development, and operation of large-scale infrastructure systems through the innovative application of artificial intelligence. We believe that AI will radically transform the way that software is built, evolved, and operated, and we want to ensure that Google makes this transition successfully.
We focus on making AI work well in the largest, most critical infrastructure systems at Google.
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Help manage, coach, and mentor a newly expanded team as we build out our management bench.
- Define and lead agile "strike team"/"tiger team" execution models. Over time, you will scale this model to address broader challenges, including improving code quality, reducing test execution times, autonomously finding and fixing bugs, and providing surge capacity to build critical new features.
- Ensure our engagements go beyond one-off consulting. Guide the team to extract, generalize, and maintain reusable tools, agentic frameworks, and AI-native infrastructure that can scale across Google's broader ecosystem.
- Guide the team as they drop into complex, brown-field systems to prove out AI-driven system optimizations (e.g., compute and storage efficiencies).
- Focus on career development, team integration, and establishing a healthy, high-performing culture centered around pragmatic, AI-native engineering.