Scribd
Software Engineer (Backend, Python) - Content Understanding
San Francisco, California, USA · Mid · full-time
Stay score
odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~45% per draw at Level III). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
The bottom of this range ($126,000) is below the Level I prevailing wage of $137,093. An H-1B cannot be filed below the prevailing wage, so an offer at the floor of this band could not be sponsored as posted.
DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
About the role
- We're seeking a Software Engineer II with strong backend development experience and a passion for solving complex data challenges at scale.
- This role offers the opportunity to work on cutting-edge generative AI and metadata enrichment problems at a truly global scale.
- Tech Stack: Our team uses various technologies.
Responsibilities
- Design and build scalable systems to extract, enrich, and process metadata from millions of documents, images, and audio content.
- Collaborate with cross-functional teams, including ML engineers and product managers, to deliver scalable, efficient, and reliable metadata solutions.
- Optimize and refactor existing systems for performance, scalability, and reliability.
- Manage and maintain data pipelines, security and infrastructure
- We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas.
- Come join us in building something meaningful.
Requirements
- Employees must have their primary residence in or near one of the following cities.
- Our four products - Scribd, Slideshare, Everand, and Fable - help billions of people across the globe move beyond access and into insight, application, and expertise.
Compensation
- In the state of California, the reasonably expected salary range is between $126,000 [minimum salary in our lowest geographic market within California] to $196,000 [maximum salary in our highest geographic market within California].
- In the United States, outside of California, the reasonably expected salary range is between $103,500 [minimum salary in our lowest US geographic market outside of California] to $186,500 [maximum salary in our highest US geographic market outside of California].
- In Canada, the reasonably expected salary range is between $131,500 CAD[minimum salary in our lowest geographic market] to $174,500 CAD[maximum salary in our highest geographic market].
Benefits
- We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs.
Company info
- The ML Content Understanding team powers metadata extraction, enrichment, and content understanding across all Scribd brands.
- We process hundreds of millions of documents, billions of images, and deliver high-quality metadata to enable content discovery and trust for millions of users worldwide.
- Our systems operate at massive scale, supporting diverse datasets like user-generated content (UGC), ebooks, audiobooks, and more.
- We work at the intersection of machine learning, data engineering, and distributed systems, collaborating closely with applied research and product teams to deploy scalable ML and LLM-powered solutions in production.
This listing is sourced directly from Scribd's careers page and normalized into a canonical job model.