Director of Applied Science and Engineering – Knowledge Graphs, AI

Role Overview

• 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.
• 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.
• Drive the design of per-tenant knowledge graph schemas, ontologies, and data models tailored to the sales execution domain.
• 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 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.
• 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.
• 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.
• 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.
• Keep the team at the frontier of knowledge graph research. Foster connections with the academic community through conference participation, publications, and strategic academic partnerships.

Requirements

• 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.

Tech Stack

• Neo4j
• Python
• Go

Benefits

• Foundational Leadership: You will define how Outreach thinks about knowledge representation and contextual reasoning, decisions that shape the platform for years.
• Greenfield Architecture: Build the knowledge graph platform from the ground up with the latitude to make foundational technical decisions on schema design, graph infrastructure, and reasoning systems.
• Scale & Impact: 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.
• Executive Visibility: Direct exposure to top leadership in the company. Present research direction and results at the executive level.
• World-Class Team: Join a culture that values scientific rigor, engineering excellence, and intellectual honesty. Collaborate with senior engineers, product leaders, and data scientists who care deeply about getting it right.
• Growth into executive level: For the right leader, this role is a path to executive level as the function scales. You will shape not just the technology but the organizational structure of applied science at Outreach.