Software Engineer - Backend; Python

Overview

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.

Culture at Scribd, Inc. 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. We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture.

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

We are looking for team members who align with our GRIT framework — Goals, Results, Innovative ideas, and Team — to drive long-term success and collaboration.

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. Our systems operate at massive scale, supporting diverse datasets like user-generated content (UGC), ebooks, audio books, 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.

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, Inc., your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor…