Director of Applied Science and Engineering - Knowledge Graphs & AI - Outreach.io

About the Role:

We are looking for a Director of Applied Science and Engineering to lead the vision, strategy, and execution of Outreach's Knowledge Graph and contextual AI capabilities. This is a senior leadership position for someone who combines deep technical expertise in knowledge representation, graph-based learning, and reasoning systems with the ability to build, inspire, and scale a high-performing team.

You will own the end-to-end technical direction of a per-tenant contextual knowledge graph that captures the full complexity of each customer's sales environment: accounts, deals, contacts, rep behaviors, competitive landscape, and the signals buried in calls, emails, and CRM activity. This graph is the reasoning backbone of the platform, powering next-best-action recommendations, deal risk signals, coaching suggestions, competitive intelligence, and agentic AI workflows. In this role, you will set the research agenda, define the architecture, hire and grow the team, and drive measurable business impact through applied science innovation.

Your Daily Adventures Will Include:

• Technical Vision & Strategy: Define and own the multi-year technical roadmap for Outreach's Knowledge Graph platform, including entity resolution, temporal reasoning, graph-based learning, and contextual inference. Translate business objectives into a coherent applied science strategy that balances research ambition with production delivery. • Team Leadership: Build, hire, and lead a team of applied scientists and research engineers. Establish team culture, research rigor, career development frameworks, and a high bar for both scientific quality and production impact. Mentor senior ICs into technical leaders. • Knowledge Graph Architecture: Drive the design of per-tenant knowledge graph schemas, ontologies, and data models tailored to the sales execution domain. Own decisions on graph databases, query languages, storage engines, and tenant isolation strategies at scale. • Information Extraction at Scale: Oversee pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, event detection, and entity linking. • Reasoning & Inference Systems: Lead the development of reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, competitive intelligence, and agentic AI decision-making. • Representation Learning & Graph ML: Direct research into graph-based models (GNNs, relational embeddings, link prediction, temporal graph networks) over heterogeneous, multi-relational graph structures to support downstream reasoning, retrieval, and recommendation tasks. • Cross-functional Leadership: Partner with leaders in Engineering, Product, Design, and Data to align science investments with product priorities. Represent the