Director of Applied Science and Engineering – Knowledge Graphs, AI

This is a fully remote position, open to applicants in India.

📋 Description

• Define and take ownership of the multi-year technical roadmap for Outreach's Knowledge Graph platform, incorporating entity resolution, temporal reasoning, graph-based learning, and contextual inference. Transform business goals into a comprehensive applied science strategy that balances research aspirations with production deliverables.

• Build, recruit, and guide a team of applied scientists and research engineers. Cultivate team culture, establish research rigor, implement career development frameworks, and uphold high standards for both scientific excellence and production impact. Mentor senior individual contributors into technical leaders.

• Spearhead the design of per-tenant knowledge graph schemas, ontologies, and data models specifically tailored for the sales execution domain.

• Supervise pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), which includes coreference resolution, relation extraction, event detection, and entity linking.

• Lead the creation of reasoning and inference layers on the knowledge graph to facilitate 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.

• Collaborate with leaders in Engineering, Product, Design, and Data to ensure alignment of scientific investments with product priorities. Represent the applied science function in executive reviews, roadmap planning, and technical design reviews.

• Establish processes and infrastructure to transition from research exploration to production deployment, including experiment tracking, model evaluation frameworks, A/B testing, and continuous model improvement loops.

• Keep the team at the forefront of knowledge graph research by fostering relationships 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.

• Over 10 years of experience in applied science or machine learning, including at least 3 years in a leadership role managing teams of 5 or more applied scientists or research engineers.

• Proven track record of developing and deploying knowledge graph, NLP, or graph ML systems at production scale—not only publishing papers but also achieving measurable business results.

• Extensive expertise in at least three of the following areas: knowledge graph construction, entity resolution, information extraction, graph neural networks, temporal reasoning, representation learning, or recommender systems.

• Strong engineering fundamentals, capable of writing production-quality code beyond simple prototype notebooks. Proficiency in Python/Golang and familiarity with graph databases or query languages (e.g., Neo4j, SPARQL, Cypher) is essential.

• Experience in recruiting, developing, and retaining top applied science talent. You have successfully nurtured individual contributors into senior technical leaders and built teams with a robust shipping culture.

• Excellent executive communication skills, able to translate complex research concepts into narratives that demonstrate business impact for C-suite and board audiences.

• Ability to navigate deep ambiguity, defining the problem space rather than just solving well-scoped issues. You excel in charting new technical directions from the ground up.

• Strong ownership mentality: Take full responsibility for research and model development initiatives, from problem formulation and data analysis to experimentation, production deployment, and ongoing performance monitoring, driving results with minimal oversight.

🏝️ Benefits

• Foundational Leadership: You will shape Outreach's approach to knowledge representation and contextual reasoning, making decisions that will influence the platform for years to come.

• Greenfield Architecture: Build the knowledge graph platform from the ground up, with the freedom to make foundational technical decisions regarding schema design, graph infrastructure, and reasoning systems.

• Scale & Impact: Outreach handles millions of sales interactions across over 4,000 enterprise customers. The work of your team will directly enable agentic AI workflows that transform how revenue teams operate globally.

• Executive Visibility: Gain direct access to top leadership within the company. Present research directions and outcomes at the executive level.

• World-Class Team: Join a culture that prioritizes scientific rigor, engineering excellence, and intellectual honesty. Collaborate with senior engineers, product leaders, and data scientists who are deeply committed to achieving the best results.

• Growth into executive level: For the right leader, this role offers a pathway to executive-level positions as the function scales. You will not only influence the technology but also shape the organizational structure of applied science at Outreach.