Director of Applied Science and Engineering - Knowledge Graphs & AI

Outreach, founded in 2014, is a pioneering agentic AI platform designed to empower revenue teams by automating workflows across sales motions such as prospecting, deal acceleration, retention, and forecasting. Leveraging advanced AI technologies including conversation intelligence and assistive AI, Outreach enables sellers to focus on strategic conversations while providing revenue leaders with comprehensive account visibility, performance insights, and improved forecasting accuracy. Leading enterprises like Databricks, SAP, Siemens, and Verizon rely on Outreach to drive their revenue operations.

Key Responsibilities:
- Define and lead the multi-year technical vision and strategy for Outreach’s Knowledge Graph platform, focusing on entity resolution, temporal reasoning, graph-based learning, and contextual inference.
- Build, manage, and mentor a high-performing team of applied scientists and research engineers, fostering a culture of scientific rigor and production impact.
- Architect per-tenant knowledge graph schemas, ontologies, and data models tailored to sales execution, making critical decisions on graph databases, query languages, and tenant isolation at scale.
- Oversee large-scale information extraction pipelines that convert unstructured conversational and document data into structured knowledge, including coreference resolution, relation extraction, and entity linking.
- Lead the development of reasoning and inference systems powering next-best-action recommendations, deal risk scoring, coaching, competitive intelligence, and agentic AI workflows.
- Drive research in graph-based machine learning models such as GNNs, relational embeddings, and temporal graph networks to support downstream reasoning and recommendation tasks.
- Collaborate cross-functionally with Engineering, Product, Design, and Data teams to align applied science initiatives with product priorities and represent the function in executive-level discussions.
- Establish robust research-to-production pipelines including experiment tracking, model evaluation, A/B testing, and continuous improvement frameworks.
- Maintain engagement with the academic community through conferences, publications, and strategic partnerships to keep the team at the forefront of knowledge graph research.

Required Qualifications:
- PhD in Computer Science, Machine Learning, NLP, or related fields with expertise in knowledge representation, reasoning, graph neural networks, information extraction, recommender systems, or conversational AI.
- Over 10 years of applied science or machine learning experience, including at least 3 years in leadership managing teams of 5+ applied scientists or research engineers.
- Proven success in building and deploying knowledge graph, NLP, or graph ML systems at production scale with measurable business impact.
- Deep expertise in at least three areas such as knowledge graph construction, entity resolution, information extraction, graph neural networks, temporal reasoning, representation learning, or recommender systems.
- Strong engineering skills with proficiency in Python and Golang, and experience with graph databases or query languages like Neo4j, SPARQL, or Cypher.
- Demonstrated ability to recruit, develop, and retain top applied science talent, growing individual contributors into senior technical leaders.
- Excellent executive communication skills, capable of translating complex research into business narratives for C-suite audiences.
- Comfort with ambiguity and defining new problem spaces, taking full ownership of research and model development from conception through deployment and monitoring.

Preferred Qualifications and Benefits:
- Experience building multi-tenant knowledge graph systems with customer isolation and scalability considerations.
- Background in sales, revenue, or B2B SaaS domains, with familiarity in deal cycles, pipeline management, and CRM data models.
- Experience integrating knowledge graphs with large language model-based systems, including retrieval-augmented generation and agentic frameworks.
- Strong publication record in top-tier conferences such as KDD, NeurIPS, ACL, EMNLP, ICLR, or SIGIR.
- Expertise in graph database performance tuning, query optimization, and multi-region deployment.
- Familiarity with Model Context Protocol (MCP) or similar agent-tool integration patterns.
- Proven track record of building applied science teams from the ground up.

Why Join Outreach?
This role offers the opportunity to define foundational technical directions for Outreach’s knowledge representation and contextual reasoning platform. You will lead greenfield architecture development, impacting millions of sales interactions across thousands of enterprise customers worldwide. The position provides direct exposure to executive leadership and a chance to grow into an executive role, shaping both technology and organizational structure. Outreach fosters a culture of scientific rigor, engineering excellence, and intellectual honesty, with a strong commitment to diversity and inclusion. Benefits include a competitive salary, generous vacation and leave policies, comprehensive medical coverage, life and accident insurance, parental leave, employee stock options, and vibrant employee resource groups supporting diverse communities.

Outreach is an equal opportunity employer dedicated to building diverse teams that bring fresh perspectives and elevate innovation. The hiring process may involve AI-assisted tools to support application review, but all final decisions are made by human recruiters.