Principal Engineer, Context & Knowledge Systems

Medallia is the pioneer and market leader in Experience Management. Our award-winning SaaS platform, Medallia Experience Cloud, leads the market in the management of experiences, insights, and actions for candidates, customers, employees, patients, and residents alike.   We believe that every experience is a memory that can last a lifetime. Experiences shape the way people feel about a company. And they greatly influence how likely people are to advocate, contribute, and stay. At Medallia, we are committed to creating a world where organizations are loved by their customers and their employees. We empower exceptional people to create extraordinary experiences together.  Bring your whole self.

At Medallia, we help the world’s leading organizations understand and improve customer and employee experiences through real-time intelligence, analytics, and AI-driven action. As enterprise AI evolves, context quality is becoming the defining factor in delivering trustworthy, personalized, and actionable AI experiences. We are building the next generation of enterprise AI systems where memory, grounding, semantic understanding, and organizational context become core infrastructure. We are looking for a Principal Context & Knowledge Systems Engineer to define and build the knowledge architecture powering AI across Medallia.   Mission Solve enterprise context engineering at scale. This role is responsible for designing the foundational systems that enable AI to understand organizational knowledge, relationships, permissions, history, and real-time signals with high accuracy and relevance. As AI systems increasingly depend on context quality rather than raw model capability, this role will lead the architecture for retrieval, memory, semantic understanding, and grounding across the enterprise. You will build the intelligence layer that connects enterprise knowledge, user context, organizational structure, and live operational signals into reliable, permission-aware AI experiences.

Enterprise Knowledge Architecture Design and build enterprise-scale knowledge graph architectures representing organizational relationships, entities, workflows, and business context Define semantic models, ontologies, metadata standards, and taxonomy strategies across products and platforms Build cross-system semantic federation capabilities connecting fragmented enterprise knowledge sources Develop frameworks for knowledge normalization, enrichment, lineage, and governance Retrieval & Context Systems Architect and scale advanced Retrieval-Augmented Generation (RAG) infrastructure Build hybrid vector and graph-based retrieval systems optimized for enterprise-scale knowledge discovery Develop intelligent context ranking, relevance scoring, compression, and summarization pipelines Implement low-latency retrieval systems supporting real-time AI interactions and workflows Optimize grounding accuracy, recall, precision, and contextual relevance across AI experiences Memory & Identity-Aware Intelligence Build session, user, team, and organizational memory systems enabling persistent contextual intelligence Design identity-aware retrieval and permission-sensitive grounding architectures Develop personalized context systems that adapt to organizational roles, historical interactions, and business workflows Implement secure context propagation and access-aware reasoning across distributed systems Real-Time Context Enrichment Design streaming pipelines that enrich AI workflows with real-time operational signals and behavioral context Build event-driven enrichment systems integrating telemetry, workflows, and customer interaction data Develop mechanisms for continuous context updating, freshness management, and temporal reasoning Create infrastructure enabling AI systems to dynamically adapt based on evolving enterprise state Platform, Governance & Technical Leadership Establish architectural standards and best practices for