Software Engineer II; Backend + Data pipelines

Join to apply for the Software Engineer II (Backend + Data pipelines) role at Scribd, Inc.

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 three products:
Everand, Scribd, and Slideshare.

We support a culture where our employees can be real and 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 balance individual flexibility with community connections through Scribd Flex, allowing employees to choose their daily work style in partnership with their manager. Occasional in-person attendance is required for all Scribd employees, regardless of location.

We hire for GRIT — Goals, Results, Innovative ideas, and Team collaboration. We are looking for candidates who demonstrate these traits in their work.

The ML Data Engineering team powers metadata extraction, enrichment, and content understanding across Scribd brands. We process hundreds of millions of documents and billions of images to deliver high-quality metadata for discovery and trust for millions of users worldwide.

We work at scale across UGC, ebooks, audio books, and more, combining machine learning, data engineering, and distributed systems. We deploy scalable ML and LLM powered solutions in production in collaboration with applied research and product teams.

We are seeking a Software Engineer II with strong backend development experience and a passion for solving complex data challenges  will design, build, and optimize distributed systems that extract, enrich, and process metadata for a wide range of content. You will work with ML engineers, product managers, and cross functional partners to integrate machine learning models and LLM based services into production pipelines and deliver impactful, high performance solutions.

This role offers the opportunity to work on cutting edge generative AI and metadata enrichment problems at a global scale.

Python, Scala, Ruby on Rails, Airflow, Databricks, Spark, HTTP APIs, AWS (Lambda, ECS, SQS, Elasti Cache, Sage Maker, Cloud Watch, Datadog) and Terraform.

• Design and build scalable systems to extract, enrich, and process metadata from millions of documents, images, and audio content.
• Leverage LLMs to integrate capabilities like summarization, classification, extraction, and enrichment into metadata pipelines.
• Collaborate with cross-functional teams to deliver scalable, efficient, and reliable metadata solutions.
• Optimize and refactor existing systems for performance, scalability, and reliability.
• Ensure data accuracy, integrity, and quality through automated validation and monitoring.
• Participate in code reviews and maintain high-quality standards in the codebase.
• Manage and maintain data pipelines, security, and infrastructure.

• 4+ years of professional software engineering experience
• Proficiency in Python, Scala, Ruby, or similar languages
• Experience designing and building distributed systems at scale
• Hands-on experience building, deploying, and optimizing solutions using ECS, EKS, or AWS Lambda
• Experience with infrastructure-as-code tools like Terraform (or similar)
• Experience working with a public cloud provider (AWS, Azure, or Google Cloud)
• Familiarity with data processing frameworks like Spark or Databricks for large-scale workloads
• Proven ability to test, profile, and optimize systems for performance, scalability, and reliability
• Bachelor’s degree in Computer Science or equivalent professional experience
• Bonus:
  Experience working with LLMs or integrating ML models into production systems

At Scribd, your base pay is one part of your total compensation package and is determined within a range. Salary ranges vary by location and level.

United States (outside California): $103,500 - $186,500;
California: $126,000 - $196,000. In Canada: $131,500 CAD - $174,500 CAD. These ranges are based on local labor benchmarks; final pay is determined by experience and role…