Software Engineer - Backend; Python

Position: Software Engineer - Backend (Python)
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 bold; we debate and commit as we embrace plot twists; and every employee is empowered to take action as we prioritize the customer.
We believe in balancing individual flexibility and community connections. Through our flexible work benefit,  Scribd Flex , employees can choose the daily work‑style that best suits their needs.
Occasional in‑person attendance is required for all Scribd employees, regardless of their location.
So what are we looking for in new team members? We hire for "GRIT" – the intersection of passion and perseverance toward long‑term goals. We seek individuals who set and achieve  G oals, reach  R esults, bring  I nnovative ideas, and positively influence the broader  T eam through collaboration and attitude.
About The Team  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.

Role Overview  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.
Tech Stack  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.

Key Responsibilities   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.
Requirements   5+ years of professional software engineering experience in 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.
Compensation  In the United States, the base pay is determined within a range based on local cost of labor benchmarks.…