Software Engineer II; Backend + Data pipelines

Scribd, Inc. is on a mission to advance human understanding. Our four products—Scribd®, Slide Share®, Everand™, and Fable—help billions of people across the globe move beyond access and into insight, application, and expertise.

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.

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 strong backend development experience and a passion for solving complex data challenges  this role, you’ll design, build, and optimize distributed systems that extract, enrich, and process metadata for a wide range of content. You’ll work closely 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 truly global scale.

Our team uses various technologies. The following are the ones that we use on a regular basis:
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, including ML engineers and product managers, 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, ensuring best practices are followed and maintaining high‑quality standards in the codebase.
• Manage and maintain data pipelines, security, and infrastructure.

• 5+ 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, 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 benchmarks for each…