Job Description:

About the Role:

Storyful is building the next generation of data and AI products on top of complex, high-volume, multi-source content. We are looking for a hands-on Senior Data Infrastructure Engineer to build the technical foundations that make those products scalable, reliable, and ready for production.

This is a senior individual contributor role for someone who is strongest in data engineering but comfortable operating across AI infrastructure, retrieval systems, cloud architecture, and product delivery. You will design and build the ingestion, processing, storage, and serving layers that power future AI and data products across Storyful.

This role will help Storyful move from promising AI features to durable AI products. The person in this role will lay the foundation that allows ML, GenAI, and agentic capabilities to work reliably on top of real-world data at production scale.

You Will:

• Work closely with machine learning engineers, software engineers, product managers, and leadership to turn raw structured and unstructured data into trustworthy product capabilities.
• Design and build scalable batch and streaming pipelines for structured, semi-structured, and unstructured data
• Own the ingestion and processing architecture for documents, text, metadata, and other content sources
• Build robust data workflows for parsing, chunking, enrichment, indexing, and retrieval
• Create the platform foundations for AI products, including orchestration, data quality, observability, lineage, and cost-aware processing
• Design storage patterns across object stores, relational databases, search/vector systems, and where appropriate graph or knowledge-based systems
• Partner with ML and product engineering to productionise AI features, agentic workflows, and retrieval-backed user experiences
• Define data contracts, schema evolution practices, and quality controls across services and teams
• Improve reliability, freshness, and traceability of pipelines that feed customer-facing products
• Contribute hands-on code while helping set engineering standards and mentoring other engineers

What good looks like in this role:

• Raw inputs from multiple sources become clean, versioned, monitorable assets that product and ML teams can trust
• New datasets and content types can be onboarded quickly without fragile one-off pipelines
• AI product features are built on observable, debuggable foundations rather than opaque glue code
• Document processing and retrieval quality improve because content is structured well before it reaches the model layer
• The team has clear standards for pipeline reliability, schema management, testing, and deployment