Director of Applied Science and Engineering - Knowledge Graphs & AI

Outreach.io

IndiaremotePosted 2 days ago

Skill Required

Director-of-Applied-ScienceDirector-Of-Machine-LearningExecutive-AI-Research-DirectorKnowledge-Graph-EngineerDirector-Of-Applied-AIApplied-AI-DirectorDirector-Of-AI-EngineeringDirector-AI-EngineeringDirector-Of-Artificial-IntelligenceAI EngineerEngineeringandAIMachine LearningDeep Learningrelated fieldStatisticsDatabricksdevelopingObservabilitybuildingTestNGPythonGolangdesignNeo4jjQueryNLPSAPFulltime

Key highlights

Required experience: 10+ years in applied science/ML, 3+ years in people leadership
Notable requirement: PhD in CS, ML, NLP, or related field
Key benefit: RSU program and competitive salary
Key benefit: 25 days annual vacation time
Role level: Senior leadership/Director with path to executive level

Role overview

Outreach is a complete agentic AI platform for revenue teams that automates workflows across prospecting, deal acceleration, and forecasting for world-leading enterprise organizations. The company is seeking a Director of Applied Science and Engineering to lead the vision, strategy, and execution of Outreach's Knowledge Graph and contextual AI capabilities. This senior leadership position involves owning the end-to-end technical direction of a per-tenant contextual knowledge graph that captures customer sales environments to power next-best-action recommendations, deal risk signals, and agentic AI workflows. The role encompasses setting the research agenda, defining architecture, and scaling a high-performing team of applied scientists and research engineers.

Responsibilities

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.
Own decisions on graph databases, query languages, storage engines, and tenant isolation strategies 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.
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.
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.

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. Ability to 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. Proven ability to grow ICs into senior technical leaders and build teams with a strong shipping culture.
Executive communication skills. Ability to translate complex research concepts into business impact narratives for C-suite and board audiences.
Comfort with deep ambiguity; ability to define the problem space and charter new technical directions from scratch.

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).

Benefits

Highly competitive salary
25 days annual vacation time + sick time and casual leave
Group medical policy coverage available to employees and up to 5 eligible family members
OPD benefit covered up to INR 10,000
Life insurance and personal accident insurance at 3x annual CTC
26 weeks of maternity leave pay, and 15 days of paternity leave pay
Opportunity to be part of company success via the RSU program
Diversity and inclusion programs that promote employee resource groups like OWN+ (Outreach Women's Network), Adelante (Latinx community), OBX (Outreach Black Connection), Mosaic (AAPI community), Pride (LGBTQIA+), Gender+, Disability Community, and Veterans/Military
Employee referral bonuses
Fun company and team outings
Foundational Leadership: Define how Outreach thinks about knowledge representation and contextual reasoning.
Greenfield Architecture: Build the knowledge graph platform from the ground up.
Scale & Impact: Work on a platform processing millions of interactions across 4,000+ enterprise customers.
Executive Visibility: Direct exposure to top leadership in the company.
World-Class Team: Culture valuing scientific rigor, engineering excellence, and intellectual honesty.
Growth into executive level: Path to executive level as the function scales.

Additional details

Outreach is an equal opportunity employer.
Applicants will be considered regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Company encourages applicants from historically marginalized groups to apply even if they do not meet all requirements.
Originally posted on Himalayas