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. About the Team The Data Platform team sits within Scribd's Infrastructure organization and is responsible for the data infrastructure, governance, and enablement that powers the entire company. We own the foundational layers that make trusted data accessible — from our Medallion Architecture and Unity Catalog in Databricks, to the semantic and AI layers that sit on top. This high‑impact team is at the center of Scribd's AI adoption push, moving fast, setting standards, and ensuring data works for engineers, analysts, and business users alike. About the Role We're looking for a Senior AI Data Engineer to lead the AI engineering workstreams on the Data Platform team. This role has three distinct dimensions: building the data infrastructure that enables AI use cases across the company (AI in the data layer), supporting platform stakeholders in building and shipping data products faster with AI (AI in the data platform), and helping our own team accelerate development through AI tooling (AI in the development lifecycle). What You’ll Do Own the deployment path for Databricks Apps — creating the infrastructure and guardrails that let non-technical users bring their AI applications to production safely and consistently. Build the AI layer on top of Scribd's Medallion Architecture and Semantic Layer — connecting AI agents and agentic workflows to governed data, and enabling non-technical users to get self‑service answers on top of it. Build AI skills and agents on top of our existing declarative tooling — giving platform stakeholders the interfaces they need to ship pipelines faster without deep expertise in every layer of the stack. Partner with teams across the organization to identify the right AI tools, frameworks, and agentic patterns that accelerate data product development and broader AI adoption. Identify and embed AI tools into how the Data Platform team writes, tests, and ships code — making AI‑assisted development a standard part of our engineering workflow. Establish guardrails to ensure AI‑generated code, queries, and pipelines are correct, consistent, and production‑ready. Help define and evolve data modeling and metadata patterns required to support AI use cases (e.g., context, documentation, discoverability). Mentor other engineers and help define what great AI data engineering looks like at Scribd. Tech Stack We use Python, SQL, Databricks (Unity Catalog, Databricks Apps, Databricks SQL, DABs, Genie Spaces), Apache Airflow, Spark, Terraform/Spacelift, and AWS. You Have 5+ years of data engineering experience, with at least 1 year focused on AI/ML infrastructure or LLM‑powered applications. Strong proficiency in Python and SQL; comfort working across the full data stack from ingestion to serving. Hands‑on experience with Databricks and cloud data platforms (Unity Catalog experience a strong plus). Experience building or integrating NLP/LLM‑based systems — RAG pipelines, semantic search, agent frameworks, or natural language interfaces. Working knowledge of how modern LLMs are trained, aligned and evaluated (RLHF, fine‑tuning, prompt engineering, retrieval patterns) and the judgment to know when each approach is the right tool. A solid understanding of data governance, access control, and what it means to build on top of trusted data. A security‑first mindset when building AI surfaces, including secret management, encryption at rest and in transit, and responsible handling of sensitive and PII data across AI pipelines and workflows. The ability to work autonomously on ambiguous problems and drive them to production. Strong communication skills — you can explain complex systems to technical and non‑technical audiences alike. Nice to Have Familiarity with metrics‑as‑code frameworks or semantic layer tooling (e.g., Statsig, dbt Semantic Layer). Prior experience in a platform or infrastructure team serving internal stakeholders. Experience evaluating AI model outputs, including building eval harnesses, defining quality metrics, and catching regressions before they reach production. Working knowledge of Terraform, or strong systems thinking to reason confidently about infrastructure changes without necessarily owning the code. Compensation In the state of California, the reasonably expected salary range is between $155,000 and $235,000. In the United States, outside of California, the range is between $127,500 and $224,000. In Canada, the range is between $162,000 CAD and $205,000 CAD. This position is also eligible for competitive equity ownership and a comprehensive benefits package. Benefits Scribd Flex (flexible work model). Comprehensive health, dental, and vision coverage. Mental health support and disability coverage. Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals. Paid parental leave and family support benefits. Retirement matching and employee equity. Learning and development programs and professional growth opportunities. Wellness and home office stipends. Complimentary access to the Scribd, Inc. suite of products. Enterprise access to leading AI tools. Required 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, Toronto, Vancouver. Mexico: Mexico City. We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing about the need for adjustments at any point in the interview process. Scribd, Inc. is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful. #J-18808-Ljbffr