Director of Applied Science and Engineering - Knowledge Graphs & AI

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 applied science function in executive reviews, roadmap planning, and technical design reviews.
• Research-to-Production Pipeline: Establish processes and infrastructure for moving from research exploration to production deployment: experiment tracking, model evaluation frameworks, A/B testing, and continuous model improvement loops.
• Industry & Academic Engagement: Keep the team at the frontier of knowledge graph research. Foster connections with the academic community through conference participation, publications, and strategic academic partnerships.

Our Vision Of You

• PhD in Computer Science, Machine Learning, NLP, or a related field with a focus on knowledge representation and reasoning, graph neural networks, information extraction, recommender systems or conversational AI and dialogue systems
• 10+ years of experience in applied science or machine learning, with at least 3 years in a people leadership role managing teams of 5+ applied scientists or research engineers.
• Demonstrated track record of building and shipping knowledge graph, NLP, or graph ML systems at production scale: not just publishing papers, but delivering measurable business outcomes.
• Deep expertise in at least three of: knowledge graph construction, entity resolution, information extraction, graph neural networks, temporal reasoning, representation learning, or recommender systems.
• Strong engineering fundamentals. You can write production-quality code, not just prototype notebooks. Proficiency in Python / Golang; and graph databases or query languages (e.g., Neo4j, SPARQL, Cypher) is required.
• Experience recruiting, developing, and retaining top applied science talent. You have grown ICs into senior technical leaders and built teams with a strong shipping culture.
• Executive communication skills. You can translate complex research concepts into business impact narratives for C-suite and board audiences.
• Comfort with deep ambiguity. You will define the problem space, not just solve well-scoped problems. You thrive when chartering new technical directions from scratch.
• Strong Ownership: Take end-to-end responsibility for research and model development initiatives, from problem formulation and data analysis through experimentation, production deployment, and ongoing performance monitoring, driving outcomes with minimal oversight.

Nice To Have

• Experience building multi-tenant knowledge graph systems with per-customer isolation and scale requirements.
• Background in sales, revenue, or B2B SaaS domains: understanding of deal cycles, pipeline management, and CRM data models.
• Experience integrating knowledge graphs with LLM-based systems (RAG architectures, tool-augmented generation, agentic frameworks).
• Strong communication skills with the ability to translate research concepts into product impact for cross-functional audiences.
• Publications in top-tier venues (KDD, NeurIPS, ACL, EMNLP, ICLR, WWW, SIGIR, etc.) in knowledge graphs, NLP, or graph learning.
• Experience with graph databases at scale (Neo4j, Amazon Neptune, or similar) including performance tuning, query optimization, and multi-region deployment.
• Familiarity with the Model Context Protocol (MCP) or similar agent-tool integration patterns.
• Track record of building applied science teams from scratch (0→1 team formation).