Backend Software Engineer; Python

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At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. Join our team as we create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our four products:
Everand, Scribd, Slideshare, and Fable.

We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. Scribd Flex enables employees to choose a daily work-style in partnership with their manager, balancing individual flexibility with community connections. Occasional in-person attendance is required for all Scribd employees, regardless of location.

We hire for “GRIT”—the intersection of passion and perseverance toward long-term goals. The acronym outlines the standards we hold ourselves to: Goals, Results, Innovative ideas, and a Team-oriented attitude.

The ML Data Engineering 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. We work at scale with data types from user-generated content to ebooks and more, at the intersection of machine learning, data engineering, and distributed systems, deploying scalable ML and LLM-powered solutions in production.

We’re seeking a Software Engineer II with deep experience building event-driven, distributed, and scalable systems in Python. You’ll design and optimize large-scale data and service pipelines running on AWS to support Scribd’s content enrichment and metadata systems. You’ll collaborate with cross-functional teams to design reliable backend services that integrate machine learning models and LLM-based components when needed, with opportunities to work on cutting-edge generative AI and metadata enrichment problems at a global scale.

Backend systems are primarily built in Python, leveraging AWS services such as Lambda, ECS, SQS, and Elasti Cache for event-driven and distributed processing. We also use Airflow, Spark, Databricks, Terraform, and Datadog for orchestration, data processing, and observability.

• Design and implement event-driven, distributed systems to extract, enrich, and process metadata from large-scale document and media datasets.
• Build and maintain scalable APIs and backend services for high-throughput content processing.
• Leverage AWS services (ECS, Lambda, SQS, Elasti Cache, Cloud Watch) to design and deploy resilient, high-performance systems.
• Collaborate with cross-functional teams to deliver backend solutions that power ML-driven features.
• Optimize and refactor existing backend systems for scalability, reliability, and performance.
• Ensure system health and data integrity through monitoring, observability, and automated testing.

• 5+ years of professional software engineering experience on Python or distributed systems development.
• Strong proficiency in Python (3+ years). Experience with Scala is a plus.
• Proven experience designing and building event-driven, distributed, and scalable systems.
• Hands-on experience with AWS services (ECS, Lambda, SQS, SNS, Cloud Watch, etc.).
• Experience with infrastructure-as-code tools like Terraform.
• Solid understanding of system performance, profiling, and optimization.
• Bachelor’s degree in Computer Science or equivalent professional experience.
• Bonus:
  Familiarity with data processing frameworks (Spark, Databricks) and workflow orchestration tools.
• Bonus:
  Experience integrating ML or LLM-based models into production systems.

At Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on local labor benchmarks for each role, level, and geographic location. San Francisco is our highest geographic market in the United States. Salary ranges vary by region and country as described in the job posting.

We carefully consider a wide range…