Backend Software Engineer; Python

Overview

Backend Software Engineer (Python) role at Scribd, Inc.

At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. We create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our products:
Everand, Scribd, Slideshare, and Fable. We support a culture where employees can be real and bold, debate and commit, and take action with the customer in mind. Scribd Flex allows choosing a daily work-style with in-person attendance required for all employees. The company emphasizes GRIT:
Goals, Results, Innovation, and Team through collaboration and attitude.

The ML Data Engineering team powers metadata extraction, enrichment, and content understanding across Scribd brands. We process hundreds of millions of documents, billions of images, and deliver high-quality metadata for content discovery and trust for millions of users. We operate at massive scale, supporting datasets such as user-generated content, ebooks, audio books, and more. We work at the intersection of machine learning, data engineering, and distributed systems, collaborating with applied research and product teams to deploy scalable ML and LLM-powered solutions.

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

Backend systems are primarily built in Python, using 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.

We provide salary ranges based on local benchmarks and geographic location. In California, the expected range is between $126,000 and $196,000. Outside California and in the United States, ranges vary by market. In Canada, ranges are provided in CAD. The company considers factors such as experience, skills, education, and business needs. This position may be eligible for equity and a comprehensive benefits package.

Are you based in a location where Scribd can employ you? Primary residence should be in or near listed cities in the United States, Canada, or Mexico, with surrounding metro areas within commuting…