Senior Backend Engineer; Python

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At Scribd (pronounced “scribbed”), our mission is to spark human curiosity. 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 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.

Our flexible work benefit, Scribd Flex, enables employees— in partnership with their manager — to choose the daily work style that best suits their needs. Occasional in-person attendance is required for all employees.

We hire for “GRIT”:
Goals, Results, Innovative ideas, and Positive Team 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 such as user‑generated content, 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 Senior Software Engineer 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.

• Provide technical leadership, mentorship, and guidance to engineers across the organization, driving secure coding best practices.
• Lead the design, implementation, and scaling of event-driven, distributed systems to extract, enrich, and process metadata from large-scale document and media datasets.
• Partner with Data Science, Infrastructure, ML Engineering, and Product teams to architect and deliver robust systems that balance scalability, high performance, and rapid iteration.
• Contribute to the team’s engineering strategy, identifying gaps, proposing new initiatives, and improving existing frameworks.
• 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.
• Optimize and refactor existing backend systems for scalability, reliability, and performance.
• Ensure system health and data integrity through monitoring, observability, and automated testing.

• 7+ years of professional software engineering experience with a focus on backend or distributed systems development.
• Strong proficiency in Python (5+ years). Experience with Scala is a plus.
• Expertise in designing and architecting large-scale event-driven and distributed systems.
• Strong cloud expertise 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.
• Experience leading technical projects and mentoring engineers.
• Bachelor’s degree in Computer Science or equivalent professional experience.
• Bonus:
  Familiarity with data…