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Outreach is seeking a Director of Applied Science and Engineering to lead the vision, strategy, and execution of its Knowledge Graph and contextual AI capabilities. This is a senior leadership role combining 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 signals from calls, emails, and CRM activity. This graph serves as the reasoning backbone of the platform, powering next-best-action recommendations, deal risk signals, coaching suggestions, competitive intelligence, and agentic AI workflows.
Key responsibilities include:
- Define and own the multi-year technical roadmap for the Knowledge Graph platform, including entity resolution, temporal reasoning, graph-based learning, and contextual inference.
- Build, hire, and lead a team of applied scientists and research engineers, establishing team culture, research rigor, and career development frameworks.
- Drive the design of per-tenant knowledge graph schemas, ontologies, and data models tailored to the sales execution domain, including decisions on graph databases, query languages, and storage engines.
- 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.
- Lead development of reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, and agentic AI decision-making.
- Direct research into graph-based models (GNNs, relational embeddings, link prediction, temporal graph networks) over heterogeneous, multi-relational graph structures.
- Partner with leaders in Engineering, Product, Design, and Data to align science investments with product priorities.
- Establish processes and infrastructure for moving from research exploration to production deployment: experiment tracking, model evaluation frameworks, A/B testing, and continuous improvement loops.
- Keep the team at the frontier of knowledge graph research through conference participation, publications, and academic partnerships.
Outreach processes millions of sales interactions across 4,000+ enterprise customers. Your team's work will directly power agentic AI workflows that change how revenue teams operate globally.
REQUIREMENTS:
- PhD in Computer Science, Machine Learning, NLP, or related field with focus on knowledge representation, reasoning, graph neural networks, information extraction, recommender systems, or conversational AI.
- 10+ years of experience in applied science or machine learning, with at least 3 years in people leadership 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 with 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 with ability to write production-quality code. Proficiency in Python/Golang and graph databases or query languages (e.g., Neo4j, SPARQL, Cypher) required.
- Experience recruiting, developing, and retaining top applied science talent, growing individual contributors into senior technical leaders.
- Executive communication skills to translate complex research concepts into business impact narratives for C-suite and board audiences.
- Comfort with deep ambiguity and ability to define problem spaces and charter new technical directions from scratch.
- Strong ownership: take end-to-end responsibility for research and model development initiatives from problem formulation through production deployment and performance monitoring.
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 with 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).
- Publications in top-tier venues (KDD, NeurIPS, ACL, EMNLP, ICLR, WWW, SIGIR) in knowledge graphs, NLP, or graph learning.
- Experience with graph databases at scale (Neo4j, Amazon Neptune) including performance tuning, query optimization, and multi-region deployment.
- Familiarity with Model Context Protocol (MCP) or similar agent-tool integration patterns.
- Track record of building applied science teams from scratch (0→1 team formation).
