Senior Backend Engineer; Ruby on Rails/Python - Content Foundations
Listed on 2026-05-15
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Software Development
AI Engineer, Data Engineer, Machine Learning/ ML Engineer
Location: New York
Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.
About the Team & RoleThe Content Foundations team builds the systems that power how content enters, evolves, and is delivered across Scribd. This includes everything from ingestion, metadata extraction, early quality controls, and the core artifacts that power search, recommendations, AI/ML systems, and the reading and listening experience.
Why this role is interestingYou’ll be joining a small and growing team working at the boundary between messy, real‑world content and highly structured systems, where file formats vary, metadata can be inconsistent, and scale amplifies every edge case. Scribd operates a hybrid catalog of premium publisher content and user‑generated uploads, spanning diverse formats, decade‑old systems, and modern services evolving alongside them. Decisions made at ingestion ripple across every downstream system.
Currentfocus areas
- Content quality and early‑stage validation
- Spam detection at upload time
- OCR and content extraction for ML/LLM use cases
- Evolving content formats to support downstream AI workflows
- Security hardening in partnership with Content and Infra‑Security
- Architectural improvements to how content and metadata flow across systems, including improving data observability for complex, asynchronous pipelines
- Own and drive technical initiatives: Lead the design, implementation, and scaling of core content systems, including ingestion pipelines, metadata services, and content processing workflows.
- Build scalable, reliable systems: Design robust solutions that handle diverse file formats and edge cases while maintaining high availability and strong data integrity.
- Collaborate across teams: Partner with Content Security, ML Data Engineering, Search & Discovery, the Content Library squad, and Product to build systems that balance performance, scalability, and user experience.
- Improve content quality and discoverability: Work with ML and Discovery teams to improve metadata extraction, classification, and enrichment that power personalization and search.
- Drive platform evolution: Identify architectural opportunities, propose new capabilities, and help evolve Scribd’s content platform to meet growing scale and complexity.
- Mentor and lead: Provide technical guidance across teams and help raise the bar on system design, data modeling, and production excellence.
- Champion AI‑driven engineering: Help shape how we leverage AI and LLM‑based systems within content processing, while maintaining a high bar for quality and reliability. In addition to building AI into our pipeline, you will help lead the team’s adoption of AI coding agents and advanced developer tools to accelerate how we build and scale our systems.
- 7+ years of software engineering experience, including experience navigating the trade‑offs of refactoring legacy systems while maintaining high availability.
- Experience building and scaling ingestion pipelines, ETL workflows, or document/content processing systems.
- Comfortable working with messy data and building systems resilient to real‑world inputs.
- Proficient in Ruby, Python, or Go (our stack includes all three).
- Experience with AWS (Lambda, SQS/SNS, S3, Step Functions) and distributed workflows.
- Strong database design skills (SQL and/or No
SQL), with a focus on reliability and resilience. - Able to communicate technical complexity to non‑technical stakeholders and have a track record of building consensus across cross‑functional engineering teams.
- Enjoy mentoring others and working collaboratively across teams.
- Experience with document formats (PDF, ebooks, markdown) and internals (OCR, parsing, transformation).
- Familiarity with ML/AI systems (embeddings, chunking, retrieval pipelines).
- Background in spam or content security systems.
- Scribd Flex (flexible work model)
- Comprehensive health, dental, and vision coverage
- Mental health support and disability coverage
- Generous paid time off, including vacation, sick time,…
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