Staff ML Software Engineer, YouTube Shorts Discovery, Funnel Growth Team

Role Overview

As a Staff ML Software Engineer, you will guide the development of personalized recommendations for YouTube Shorts, drive viewer growth through ML technologies, and collaborate across teams.

What You Will Do

You will design and implement ML technologies to enhance shorts recommendation quality, drive significant viewer growth, and gain experience across the entire recommendation system stack.

Why It Might Be a Fit

You will be a versatile engineer with leadership qualities, enthusiastic to take on new problems across the full-stack, and passionate about working at the intersection of cutting-edge technology and boundless creativity.

Requirements

  • Bachelor’s degree or equivalent practical experience
  • 8 years of experience in software development
  • 3 years of experience building and deploying recommendation systems models
  • Preferred qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field, 8 years of experience with data structures and algorithms, 3 years of experience in a technical leadership role, 3 years of experience working in a complex, matrixed organization

Benefits

  • 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

To apply for this job please visit www.google.com.

Tired of manual job applications?

JobCopilot auto-applies to thousands of RevOps and GTM roles on your behalf — so you can focus on interviews, not applications.

RevOps Careers Favicon

Get the latest RevOps jobs every Monday.

No noise. Just the best GTM roles, curated weekly.

We don’t spam! Read our privacy policy for more info.

Applying for this role?

Tailor your resume to this exact role — hiring managers notice the difference.

Latest articles on the blog

RECRUITERS!

Reduce the risk of your recruitment process (applicant quality, long and inefficient process) by selecting from a relevant pool of candidates.

POST A NEW JOB NOW!