Senior Software Engineer; Python + Distributed systems

Position

Senior Software Engineer (Python + Distributed systems)

About

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.

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. 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 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.

Tech Stack

• Backend systems are primarily built in Python, leveraging AWS services such as Lambda, ECS, SQS, and ElastiCache for event‑driven and distributed processing.
• Additional tools: Airflow, Spark, Databricks, Terraform, and Datadog for orchestration, data processing, and observability.

Key Responsibilities

• 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, ElastiCache, CloudWatch) 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.

Requirements

• 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, CloudWatch, 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 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…