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Engineering Manager, ML Infrastructure

Google

Engineering Manager, ML Infrastructure

Google

·

On-site

·

Full-time

·

1w ago

  • Lead our new Workload Optimization (WO) team. Set the technical goal and roadmap and drive its key features in this pivotal role.

  • Collaborate closely with teams across machine learning (ML), and our product area customers to ensure successful execution.

  • Shape the team's culture and processes, identify new opportunities, and translate our broader strategy into concrete priorities and projects.

  • Coach and provide career guidance to your reports, improve our engineering practices, and influence technical direction across the organization.

  • Navigate open-endedness and actively contribute to the team's engineering efforts as a technical leader.

  • Bachelor’s degree, or equivalent practical experience.

  • 8 years of experience in software development.

  • 3 years of experience with developing 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.

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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

3.7

25 reviews

Work-life balance

3.8

Compensation

4.2

Culture

3.4

Career

3.9

Management

2.8

68%

Recommend to a friend

Pros

Excellent compensation and benefits

Smart and talented colleagues

Great perks and work flexibility

Cons

Management and leadership issues

Bureaucracy and slow processes

Constantly changing priorities and reorganizations

Salary Ranges

57,502 data points

Junior/L3

L3

L4

L5

L6

L7

L8

Mid/L4

Principal/L7

Senior/L5

Staff/L6

Director

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