About the job
About Our Organization:
Welcome to Scribd Inc. (pronounced “scribbed”), where our passion lies in igniting human curiosity through storytelling and knowledge-sharing. We invite you to join our dynamic team as we work towards democratizing the exchange of ideas and empowering collective expertise with our innovative products: Everand, Scribd, Slideshare, and Fable.
This job posting represents an established opportunity within our organization.
At Scribd, we cultivate a culture where authenticity and boldness thrive. We value open discussions and commitment as we embrace the unexpected, empowering every employee to take initiative while keeping our customers at the forefront.
We believe in a balanced approach to work structure, merging individual flexibility with community engagement. Our Scribd Flex program allows employees, in collaboration with their managers, to choose work styles that best suit their needs. This initiative emphasizes the importance of intentional in-person gatherings to foster collaboration and connection. Thus, occasional in-person attendance is a requirement for all Scribd employees, regardless of their remote status.
What do we seek in our new teammates? We prioritize candidates who embody “GRIT” – a blend of passion and perseverance towards long-term goals. At Scribd, we encourage a GRIT-driven approach to work, where the ability to set and achieve Goals, deliver Results, contribute Innovative ideas, and positively impact the Team through collaboration is essential.
About the Team:
Our ML Data Engineering team is at the forefront of metadata extraction, enrichment, and content understanding across all Scribd products. We manage vast volumes of documents and images, ensuring high-quality metadata that enhances content discovery and builds trust among millions of users around the globe.
Our systems function on a massive scale, incorporating diverse datasets like user-generated content, ebooks, audiobooks, and more. We operate at the convergence of machine learning, data engineering, and distributed systems, working closely with applied research and product teams to deploy scalable ML solutions.

