Senior Backend Engineer (Python) - Content Understanding

About the role

The ML Content Understanding team at Scribd, Inc. 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.

Responsibilities

• Provide technical leadership, mentorship, and guidance to engineers across the organization, driving secure coding best practices.
• Lead the design, implementation, and scaling of event-driven, distributed systems to extract, enrich, and process metadata from large-scale document and media datasets.
• Partner with Data Science, Infrastructure, ML Engineering, and Product teams to architect and deliver robust systems that balance scalability, high performance, and rapid iteration.
• Contribute to the team’s engineering strategy, identifying gaps, proposing new initiatives, and improving existing frameworks.
• Build and maintain scalable APIs and backend services for high-throughput content processing.
• Leverage AWS services (ECS, Lambda, SQS, ElastiCache, CloudWatch) to design and deploy resilient, high-performance systems.
• Optimize and refactor existing backend systems for scalability, reliability, and performance.
• Ensure system health and data integrity through monitoring, observability, and automated testing.

Requirements

• 7+ years of professional software engineering experience with a focus on backend or distributed systems development.
• Strong proficiency in Python (5+ years).
• Expertise in designing and architecting large-scale event-driven and distributed systems.
• Strong cloud expertise with AWS services (ECS, Lambda, SQS, SNS, CloudWatch, etc.).
• Solid understanding of system performance, profiling, and optimization.
• Experience leading technical projects and mentoring engineers.

Bonus

• Familiarity with data processing frameworks (Spark, Databricks) and workflow orchestration tools.
• Experience integrating ML or LLM-based models into production systems.