Senior AI Data Engineer

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.

So what are we looking for in new team members? At Scribd, Inc., we hire for “GRIT.” Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude.

This posting reflects an approved, open position within the organization.

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 is a high-impact team at the center of Scribd's AI adoption push. We move fast, we set standards, and we care deeply about making data work for everyone — 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

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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

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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

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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

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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

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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

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Establish guardrails to ensure AI-generated code, queries, and pipelines are correct, consistent, and production-ready

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Help define and evolve data modeling and metadata patterns required to support AI use cases (e.g., context, documentation, discoverability)

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Mentor other engineers and help define what great AI data engineering looks like at Scribd

Tech Stack

We use a range of tools across the platform. The ones you'll work with most regularly: Python, SQL, Databricks (Unity Catalog, Databricks Apps, Databricks SQL, DABs, Genie Spaces), Apache Airflow, Spark, Terraform/Spacelift, and AWS.

You Have

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5+ years of data engineering experience, with at least 1 year focused on AI/ML infrastructure or LLM-powered applications

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Strong proficiency in Python and SQL; comfort working across the full data stack from ingestion to serving

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Hands-on experience with Databricks and cloud data platforms (Unity Catalog experience a strong plus)

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Experience building or integrating NLP/LLM-based systems — RAG pipelines, semantic search, agent frameworks, or natural language interfaces

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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

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A solid understanding of data governance, access control, and what it means to build on top of trusted data

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A security-first mindset when building AI surfaces, including secret management, encryption at rest and in transit, and