Digital library and content platform with AI-powered discovery and analytics
Scribd operates a four-product portfolio (Scribd, Slideshare, Everand, Fable) centered on digital content access and learning. The tech stack reveals a company in architectural transition: Python and Go backends, Next.js frontend adoption displacing Ruby on Rails, and heavy investment in data infrastructure (Airflow, Databricks, Delta Lake). Active projects span generative AI features, LLM-powered solutions, and metadata enrichment—paired with internal pain around monolith decomposition and legacy SPA migration—indicating a platform refactoring to support ML-first product experiences at scale.
Scribd, Inc. builds digital platforms for content discovery, sharing, and learning across a four-product suite: Scribd (digital library), Slideshare (presentation sharing), Everand (audiobook and ebook subscription), and Fable (visual storytelling). The company serves individual readers, authors, and enterprise knowledge teams, primarily monetized through subscription models. With 201–500 employees based in San Francisco, Scribd's engineering and data departments are substantially scaled, supported by active infrastructure work around ML pipelines, recommendation algorithms, and experimentation frameworks.
Primary languages are Python, Go, Scala, and Ruby on Rails; frontend uses Next.js and React. Data layer runs on Apache Airflow, Databricks, and Delta Lake. Cloud infrastructure is AWS. The company is actively adopting Next.js while phasing out Ruby on Rails.
Active projects include deploying LLM-powered solutions, generative AI features, metadata extraction, recommendation surfaces, and zero-to-one ML integrations into user-facing products. Infrastructure work targets scalable ML pipelines and improved experimentation frameworks.
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