Engineering Manager, ML/Data Engineering

Overview Be among the first 25 applicants. At Scribd Inc. (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 four products: Everand, Scribd, Slideshare, and Fable. This posting reflects an approved, open position within the organization. We support a culture where employees can be real and bold; debate and commit; and take action while prioritizing the customer. Scribd Flex allows choosing a daily work-style in partnership with managers, with occasional in-person attendance required for all employees, regardless of location. We hire for GRIT: goals, results, initiative, and teamwork. The Team and Role 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 will lead a team responsible for building scalable ML-based foundations that detect and address harmful content. You’ll help enable ML models to reason across our corpus in batch and real-time, ensuring our safety classifiers and automated policy enforcement are performant, scalable, and resilient. You will sit at the intersection of Big Data, AI, MLOps, and Platform Integrity, directly impacting the safety of millions of users. Responsibilities Lead and grow a high-performing engineering team: manage, mentor, and recruit data and ML engineers; foster technical excellence, operational rigor, and user-safety empathy. Architect scalable ML data pipelines: design distributed data processing systems for hundreds of millions of documents; support batch and real-time inference for content moderation and risk detection. Build foundational data layers: develop semantic embeddings, metadata extracts, and behavioral signals to power Content Trust ML models. Partner on AI/LLM integration: collaborate with Search & Discovery and Applied Research to integrate ML/LLM-based reasoning into trust pipelines. Drive Operational Excellence: establish SLAs for infrastructure; ensure automated enforcement systems are fast and explainable. Cross-functional Leadership: work with Product Managers (Content Trust), Legal/Policy, and Data Science to translate evolving regulatory requirements (e.g., DSA) into robust architectures. Qualifications Leadership Experience: 8+ years of engineering experience, with 3+ years in people management or technical lead within a Data or ML Engineering organization. Scale Expertise: Proven track record of production-grade data pipelines at massive scale (100M+ entities) using Spark, Flink, Kafka, or Airflow. ML Infrastructure Fluency: Deep understanding of ML lifecycle, including feature engineering, MLOps, and vector databases (e.g., Pinecone, Milvus, Weaviate). Trust & Safety Context: Experience building systems for content moderation, fraud detection, spam prevention, or digital rights management. Technical Breadth: Proficiency in Python, Scala, or Go; cloud-native infrastructure (AWS/GCP, Kubernetes) and Snowflake/BigQuery. Strategic Communication: Ability to explain architectural trade-offs to non-technical stakeholders in Legal, Policy, and Product. Bonus Points – ideally you have: LLM Pipelines: Experience with RAG pipelines or data infra for fine-tuning LLMs. UGC Experience: Large-scale User Generated Content ecosystems and unstructured data challenges. Regulatory Knowledge: Familiarity with DSA, UK Online Safety Act, etc. Adversarial Mindset: Defending against malicious actors and evolving abuse patterns. Compensation and Benefits At Scribd, base pay is part of total compensation and is determined within a range based on location. California salaries range from $163,000 to $254,500; other US locations range from $134,500 to $241,500. In Canada, ranges are CA$171,000 to CA$244,000. We consider factors such as experience, skills, education, and business needs. This position may include equity and a comprehensive benefits package. Working at Scribd Are you based in Scribd’s location requirements? Primary residence should be in or near listed cities (US, Canada, or Mexico) with surrounding metro areas within commuting distance. Benefits, Perks, and Wellbeing Healthcare insurance coverage (medical/dental/vision): 100% paid for employees 12 weeks paid parental leave Disability plans (short-term and long-term) 401k/RSP matching Onboarding stipend for home office peripherals Learning & Development allowances and programs Wellness, WiFi, and other stipends Mental Health resources and more Free Scribd subscription and other company-wide benefits Volunteer day and inclusive workplace programs Access to AI tools for productivity For more about life at Scribd, visit If you require reasonable adjustments in the interview process, email Scribd is an equal opportunity employer; we value diversity and inclusion. Related roles and referrals: Senior Manager roles and other Principal Engineering positions are listed for visibility. #J-18808-Ljbffr