Software Engineer Manager II, Resource Engineering

MINIMUM QUALIFICATIONS:

* Bachelor’s degree, or equivalent practical experience.

* 8 years of experience in software development.

* 3 years of experience with developing large-scale infrastructure, distributed

systems or networks, or experience with compute technologies, storage or

hardware architecture.

* 3 years of experience in a technical leadership role.

* 2 years of experience in a people management or team leadership role.

PREFERRED QUALIFICATIONS:

* Master's degree or PhD in Computer Science or related technical field.

* 3 years of experience working in a complex, matrixed organization.

* Strong understanding of AI/ML frameworks.

* Experience mentoring executive engineers and other technical leads.

* Experience in capacity planning for complex, large-scale distributed systems.

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.

In this role, the Google Cloud Storage (GCS) provides planet scale, object

storage service for external Google Cloud Platform (GCP) customers and internal

large-scale Google products like GMail, Photos, Drive, and YouTube. As a part of

the GCS Resource Engineering team, you will build extensible software solutions

that plan capacity to guarantee high service reliability and resource

efficiency.

As a Software Engineering Manager, you will lead a critical team dedicated to

enabling high-throughput products and scenarios, including our Bandwidth Highway

program. You will address ambiguous supply problems and ensure GCS remains the

high-performance storage backbone for the next-generation of AI workloads.Google

Cloud accelerates every organization’s ability to digitally transform its

business and industry. We deliver enterprise-grade solutions that leverage

Google’s 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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

* Manage team staffing, career growth, and uphold a high-performance bar, while

fostering an environment to build capacity planning solutions for

high-throughput GCS products.

* Own the strategy, scoping, navigation, and outcomes for capacity planning

systems targeting high-throughput scenarios, such as the Bandwidth Highway

and Rapid Cache.

* Guide the team through ambiguity by breaking down complex challenges such as

supply scarcity and dense AI zone typologies into tractable technical

milestones.

* Navigate complex relationships across GCS, Networking, Compute, and Data

Center teams to align supply agendas and ensure seamless on boarding for

massive AI/ML workloads.

* Drive engineering efforts to automate capacity adjudication, optimize network

resources, and accelerate the time required to onboard new high-throughput

customers.