refresh

Trending companies

Trending companies

Google
Google

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

Engineering Manager, YouTube Posts Discovery

RoleEngineering Manager
LevelLead
WorkOn-site
TypeFull-time
Posted1 month ago
Apply now

About the job

At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.
The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

  • Lead, manage, and grow a team of software engineers focused on posts recommendation systems on YouTube.

  • Collaborate with cross-functional partners, product experience teams, coreX and shorts discovery teams, research and infrastructure groups, and other applied ML teams across YouTube to drive the team's goal and roadmap.

  • Co-ordinate with senior Team Leader (TLs) to plan and execute project delivery, growing Posts as primary content on YouTube and enabling high-value discovery through innovative recommendations and modeling technologies.

  • Cultivate team culture and values by developing individuals and creating a collaborative and agile environment.

Minimum qualifications

  • Bachelor’s degree or equivalent practical experience.

  • 8 years of experience in software development.

  • 3 years of experience in a technical leadership role.

  • 2 years of experience in a people management role.

  • 2 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.

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

Preferred qualifications

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.

  • 3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.

Total Views

0

Total Apply Clicks

0

Total Mock Apply

0

Total Bookmarks

0

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

Mid/L4

Mid/L4 · Accessibility Analyst

1 reports

$214,500

total per year

Base

$165,000

Stock

-

Bonus

-

$214,500

$214,500

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