Engineering Manager, ML​/Data Engineering - Content Trust

About The Company

At Scribd Inc. (pronounced “scribbed”) our mission is to spark human curiosity. We create a world of stories and knowledge, democratizing the exchange of ideas and information through our products:
Everand, Scribd, Slideshare, and Fable. We support a culture where employees can be real, bold, commit to diverse ideas, and prioritize the customer. Our flexible work benefit, Scribd Flex, allows employees to choose the work style that suits their needs while ensuring occasional in-person attendance for collaboration and connection.

The ML Data Engineering team builds high-throughput, ML-driven data pipelines that process hundreds of millions of documents to detect, classify, and mitigate untrustworthy content. As the Manager of ML Data Engineering, you lead a specialized team building scalable ML foundations that enable safety classifiers and automated policy enforcement tools. You sit at the intersection of Big Data, AI, MLOps, and Platform Integrity, impacting the safety of millions of users.

• Lead and grow a high-performing engineering team: manage, mentor, and recruit a world-class team of data and ML engineers, fostering technical excellence and empathy for user safety.
• Architect scalable ML data pipelines: design and oversee distributed data processing systems handling hundreds of millions of documents, supporting batch and real-time inference.
• Build Trust scores: develop and maintain foundational data layers—semantic embeddings, metadata extracts, and behavioral signals—powering Content Trust ML models.
• Partner on AI/LLM integration: collaborate with Search & Discovery and Applied Research teams to integrate ML/LLM reasoning into trust pipelines for nuanced policy violation understanding.
• Drive operational excellence: establish SLAs for infrastructure, ensuring automated enforcement remains fast and explainable.
• Cross-functional leadership: work with Product Managers, Legal/Policy teams, and Data Science to translate regulatory requirements (e.g., DSA) into robust architectures.

• Leadership experience: 8+ years of engineering experience, 3+ years in people management or technical lead within Data or ML Engineering.
• Scale expertise: proven track record of building and operating production-grade data pipelines at massive scale (100M+ entities) using Spark, Flink, Kafka, or Airflow.
• ML infrastructure fluency: deep understanding of the ML lifecycle, feature engineering, model deployment (MLOps), and vector databases such as Pinecone, Milvus, or Weaviate.
• Trust & Safety context: prior experience building systems for content moderation, fraud detection, spam prevention, or digital rights management.
• Technical breadth: strong proficiency in Python, Scala, or Go, and experience with cloud-native infrastructure (AWS, GCP, Kubernetes, Snowflake, Big Query).
• Strategic communication: ability to explain complex architectural trade-offs to non-technical stakeholders in Legal, Policy, and Product.

• LLM pipelines: experience building RAG pipelines or managing data infrastructure for fine-tuning LLMs.
• UGC experience: background working with large-scale User Generated Content ecosystems and unique challenges of unstructured document data.
• Regulatory knowledge: familiarity with global safety regulations such as the Digital Services Act (DSA) or UK Online Safety Act.
• Adversarial mindset: experience building systems that defend against malicious actors and evolving abuse patterns.

Base pay is determined within a range based on location. In San Francisco, the range is $163,000 to $254,500. In the United States outside California, the range is $134,500 to $241,500. In Canada, the range is $171,000 CAD to $244,000 CAD. This position is eligible for equity and a comprehensive benefits package.

Employees must have a primary residence in or near one of our locations. United States:
Atlanta, Austin, Boston, Dallas, Denver, Chicago, Houston, Jacksonville, Los Angeles, Miami, New York City, Phoenix, Portland, Sacramento, Salt Lake City, San Diego, San Francisco, Seattle, Washington D.C. Canada:
Ottawa,…