Director of Applied Science and Engineering - Knowledge Graphs & AI

We Love AI Jobs; Hyderabad; Remote; INR 3,500,000 - 9,000,000; Full time

Job summary

Outreach seeks a Director of Applied Science and Engineering to lead Knowledge Graph and contextual AI capabilities for per-tenant sales environments. You will own the technical roadmap, shape architecture, and mentor a team of scientists and engineers to deliver graph-based reasoning and next-best-action capabilities.

You will drive research-to-production processes, collaborate with Engineering, Product, and Data, and maintain close ties to academic partners to stay at the frontier of knowledge

Qualifications

• PhD in Computer Science, ML, NLP, or related field.
• 10+ years of applied science or ML experience with leadership of 5+ people.
• Proven track record delivering knowledge graph/NLP/graph ML systems at production scale.

Responsibilities

• Define the multi-year technical roadmap for Outreach's Knowledge Graph platform.
• Lead a team of applied scientists and research engineers.
• Design per-tenant knowledge graph schemas and data models for the sales domain.
• Oversee extraction from unstructured data like calls and emails.
• Guide graph ML and reasoning systems to support recommendations and insights.
• Drive production readiness with experiments, evaluations, and A/B testing.

Skills

Knowledge graphs

Graph neural networks

Python

Golang

Neo4j

SPARQL

Cypher

Natural language processing

Education

PhD in Computer Science, ML, NLP, or related field

Tools

Neo4j

SPARQL

Cypher

Job description

# Director of Applied Science and Engineering - Knowledge Graphs & AIOutreachRemote (Hyderabad)Full-timeUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageYou’ll set the technical direction for Outreach’s per-customer knowledge graph and contextual AI capabilities, which support recommendations, deal insights, and agentic workflows. The role leads applied scientists and research engineers across graph architecture, information extraction, model development, and production deployment.* Permanent* Fully Remote* 10+ years* PhD### Skills and expertiseKnowledge graphsGraph neural networksPythonGolangNeo4jSPARQLCypherNatural language processing### Key responsibilities* Define the technical roadmap and architecture for Outreach’s per-tenant knowledge graph platform.* Lead a team building graph learning, information extraction, and reasoning systems for sales workflows.* Establish research-to-production processes for evaluation, deployment, testing, and ongoing model improvement.## How this job comparesCompared with 19 open director machine learning jobs on October 10, 2026.Similar open jobs: We Love AI Jobs lists 19 open director machine learning jobs at 18 companies.Remote work: This job is remote. 4 of the 19 director machine learning jobs (21%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. Building and deploying graph-based learning, reasoning, and contextual AI systems is the core of the role. The posting ties these systems directly to recommendations, deal signals, and agentic AI workflows.## Full descriptionAbout OutreachOutreach, founded in 2014, is the only complete agentic AI platform for revenue teams. Outreach infuses agentic AI, conversation intelligence, and assistive AI to power hundreds of use cases across revenue motions. From new logo prospecting to expansions, deal acceleration, driving retention, and forecasting, Outreach AI automates workflows and frees sellers to focus on more strategic conversations and actions. Revenue leaders benefit from connected account visibility, performance insights, and higher forecasting accuracy across every GTM team. World leading enterprise organizations use Outreach to power their revenue teams, including Databricks, SAP, Siemens, and Verizon to name a few.The RoleWe are looking for a Director of Applied Science and Engineering to lead the vision, strategy, and execution of Outreach's Knowledge Graph and contextual AI capabilities. This is a senior leadership position for someone who combines 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 the signals buried in calls, emails, and CRM activity. This graph is the reasoning backbone of the platform, powering next-best-action recommendations, deal risk signals, coaching suggestions, competitive intelligence, and agentic AI workflows. In this role, you will set the research agenda, define the architecture, hire and grow the team, and drive measurable business impact through applied science innovation.Your Daily Adventures Will Include:• Technical Vision & Strategy: 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.• Team Leadership: 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.• Knowledge Graph Architecture: 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.• Information Extraction 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.• Reasoning & Inference Systems: 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.• Representation Learning & Graph ML: 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.• Cross-functional Leadership: 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.• Research-to-Production Pipeline: 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.• Industry & Academic Engagement: Keep the team at the frontier of knowledge graph research. Foster connections with the academic community through conference participation, publications, and strategic academic partnerships.Our Vision Of You:• 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.Upload your resume once and Scout applies for youApply with Scout