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Google
Google

Organizing the world's information and making it universally accessible.

Software Engineering Manager, Cloud AI/ML Infrastructure

RoleMachine Learning
LevelLead
WorkOn-site
TypeFull-time
Posted3 weeks ago
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  • 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 goal and roadmap within the scope of your team. 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 partner closely with cross-functional, cross-regional teams to ensure our AI/ML infrastructure delivers exceptional value and drives success for our customers.

  • Lead the design and development of tools and software for our AI/ML Infrastructure to deliver end-to-end developer experience.

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, you will lead a talented engineering team to drive the advancement of our AI infrastructure and software. You will serve as the crucial bridge between our users and our technology, translating customer issues into technical solutions that deliver real value. As the driver of team and project success, you will establish a clear technical goal and roadmap while simultaneously mentoring, managing, and expanding your team to ensure they have the exact skill sets needed to execute our goals.

  • Bachelor’s degree or equivalent practical experience.

  • 8 years of experience in software development.

  • 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

  • 3 years of experience in a technical leadership role.

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

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About Google

Google

Google

Public

Google specializes in internet-related services and products, including search, advertising, and software.

10,001+

Employees

Mountain View

Headquarters

$1,700B

Valuation

Reviews

10 reviews

4.5

10 reviews

Work-life balance

3.2

Compensation

4.3

Culture

4.1

Career

4.2

Management

3.8

82%

Recommend to a friend

Pros

Great benefits and perks

Innovative and interesting work

Career development and learning opportunities

Cons

High pressure and expectations

Long hours and heavy workload

Fast-paced and overwhelming environment

Salary Ranges

57,503 data points

Junior/L3

L6

L7

L8

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

L3

L4

L5

Junior/L3 · Data Scientist L3

0 reports

$176,704

total per year

Base

-

Stock

-

Bonus

-

$150,298

$203,110

Interview experience

9 interviews

Difficulty

3.4

/ 5

Duration

14-28 weeks

Offer rate

44%

Experience

Positive 0%

Neutral 56%

Negative 44%

Interview process

1

Application Review

2

Online Assessment/Technical Screen

3

Phone Screen

4

Onsite/Virtual Interviews

5

Team Matching

6

Offer

Common questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Product Sense