
Staff ML Software Engineer, YouTube Shorts Discovery, Funnel Growth Team
Mountain View, CAJob$207–300K/yrSeen 5 days agoSeen in employer's feed 5 days ago
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At a glance
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Job overview
The Staff ML Software Engineer will guide personalized recommendations for YouTube Shorts and drive viewer growth by designing and implementing machine-learning technologies and algorithms. The role spans recommendation systems across major YouTube surfaces and involves cross-team collaboration. Candidates need substantial software development experience, including production recommendation systems work; a master’s degree or PhD and additional leadership and organizational experience are preferred.
Skills & qualifications
Skills
Qualifications
Full job description
Staff ML Software Engineer, YouTube Shorts Discovery, Funnel Growth Team
Share Staff ML Software Engineer, YouTube Shorts Discovery, Funnel Growth Team
corporate_fare YouTube place Mountain View, CA, USA
Advanced
Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.
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XIn most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications:
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Bachelor’s degree or equivalent practical experience.
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8 years of experience in software development.
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3 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains.
Preferred qualifications:
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Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
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8 years of experience with data structures and algorithms.
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3 years of experience in a technical leadership role leading project teams and setting technical direction.
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3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, 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.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 aboutbenefits at Google (https://www.google.com/about/careers/applications/benefits/) .
Responsibilities
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Guide the development of personalized, high-quality recommendations that will directly shape the YouTube Shorts experience for millions of users.
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Drive significant viewer growth by designing and implementing ML technologies and algorithm to enhance shorts recommendation quality across major YouTube surfaces, including Home, Watch Next, Shorts First, Notifications.
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Gain experience across the entire recommendation system stack.
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Drive cross-team and cross-functional collaborations.
Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google'sApplicant and Candidate Privacy Policy (./privacy-policy) .
Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See alsoGoogle's EEO Policy (https://www.google.com/about/careers/applications/eeo/) ,Know your rights: workplace discrimination is illegal (https://careers.google.com/jobs/dist/legal/EEOC\_KnowYourRights\_10\_20.pdf) ,Belonging at Google (https://about.google/belonging/) , andHow we hire (https://careers.google.com/how-we-hire/) .
If you have a need that requires accommodation, please let us know by completing ourAccommodations for Applicants form (https://goo.gl/forms/aBt6Pu71i1kzpLHe2) .
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.
Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also https://careers.google.com/eeo/ and https://careers.google.com/jobs/dist/legal/OFCCP_EEO_Post.pdf If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form: https://goo.gl/forms/aBt6Pu71i1kzpLHe2.
How this job compares
Set against the 6,158 open Machine Learning Engineer jobs on Olive Jobs, counted October 28, 2026.
- Pay
- This posting$207–300K/yr
- California median$174–260K/yr1,348 salary ranges
- US median$144–242K/yr4,913 salary ranges
This range's midpoint, $254K, is 17% above the California median midpoint ($218K) — in the middle half of Machine Learning Engineer salary ranges in California.
- Openings
- Olive Jobs lists 1,822 open Machine Learning Engineer jobs in California and 6,158 across the US. 7% of them nationwide are listed as remote or flexible.
- Experience
- This posting asks for at least 8 years of experience — more than three in four Machine Learning Engineer postings that state a minimum ask for (median 5 years, across 4,916 postings).
- Education
- 2,538 of the 6,158 open Machine Learning Engineer jobs (41%) ask for a bachelor's degree or higher.
Counted across the open, public US postings on Olive Jobs on October 28, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.
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