Backend Software Engineer; Python

Overview

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About The Company
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. When it comes to workplace structure, Scribd offers flexible work with Scribd Flex, allowing employees, in partnership with their manager, to choose daily work styles.

Occasional in-person attendance is required for all Scribd employees, regardless of location.

We hire for “GRIT” — Goals, Results, Innovation, and Team. We look for someone who can set and achieve goals, achieve results, contribute innovative ideas, and positively influence the team through collaboration and attitude.

We’re seeking a Software Engineer II with deep experience building event-driven, distributed, and scalable systems in Python. In this role, you’ll design and optimize large-scale data and service pipelines running on AWS, supporting Scribd’s content enrichment and metadata systems. You’ll work closely 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 truly global scale.

Our 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. Salaries vary by location:

- California: $126,000 to $196,000 - United States (outside California): $103,500 to $186,500 - Canada: $131,500 CAD to $174,500 CAD This position is eligible for equity and a comprehensive benefits package. The salary ranges reflect local labor benchmarks and may vary if the level changes. We consider experience, skills, education, and business needs when determining compensation.

Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near specific cities and metro areas.

United States:
Atlanta, Austin, Boston, Dallas, Denver, Chicago, Houston, Jacksonville, Los Angeles, Miami, New York City,…