Senior Applied Scientist - Knowledge Graphs & Ai

About Outreach

Outreach, 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.

• We are looking for an Associate Applied Scientist to join a dynamic and innovative AI platform team. If you are passionate about applying machine learning to knowledge graphs and reasoning systems at scale, this is an opportunity to build core components of Outreach's per-tenant knowledge graph while developing deepexpertiseunder the guidance of senior scientists.
• Our team is building 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 powers contextual reasoning across the platform, driving next-best-action recommendations, deal risk signals, coaching suggestions, and competitive intelligence. In this pivotal role, you will design the underlying representations, extraction pipelines, and reasoning layers that make this possible, working closely with cross-functional engineering and product teams to deliver innovative, scalable, and reliable AI capabilities with direct impact on revenue outcomes.
• This role is ideal for someone with strong ML fundamentals who wants to build deep expertise in knowledge graphs and applied NLP in a fast-moving product environment.

Your Daily Adventures Will Include:

• Knowledge Graph Design & Construction:
  Design andimplemententity resolution and ontology population within established graph schemas. Write andoptimizequeries for graph traversal and feature extraction. Own data quality for assigned domains.
• Information Extraction:
  Build pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), in including coreference resolution, relation extraction, and event detection.
  
  Run experiments to compare approaches and improveaccuracymetrics.
• Contextual Reasoning & Recommendation:
  Implement graph traversal logic and feature queries that feed downstream scoring signals. Build and maintain features for deal risk, next-best-action, or coaching recommendation surfaces.
• Representation L Learning:
  
  Train and evaluate link prediction and node classification models using established graph embedding methods. Implementevaluationpipelines and track model performance over time.
• Domain Modeling:
  Translate sales concepts, such as deal stages, buyer engagement patterns, rep behaviors, and account health, into graph nodes and relationships under the guidance of senior scientists. Contribute to ontology design and documentation.
• Cross-functional Collaboration:
  
  Work with software engineers to deploy models and pipelines into production. Write clean, tested code. Monitor system health and respond to incidents. Participate in code review and design discussions.

• PhD in a relevant field such as Computer Science, NLP, Machine Learning, or a related discipline with a focus on knowledge representation and reasoning, information extraction and relationship extraction,graph neural networks,recommendation systems, orconversationAI and dialogue systems.
  
  MS + 2 years of relevantexperiecnewill also be considered.
• Solid engineering fundamentals. You can write production-quality code, not just prototype notebooks. You can write clean, tested Python code. Experience with graph databases or query languages (e.g., Neo4j, SPARQL, Cypher).
• Demonstrated ability to build and evaluate ML models. You've trained models, measured performance using appropriate metrics, and iterated on results.
• A track record of building things: whether that's research prototypes that went beyond the paper, open-source contributions, or side projects that required real systems thinking. You understand the gap between a research prototype and a reliable production system, such as monitoring, data drift, latency, and operational excellence.
• Strong Ownership:
  Take end-to-end responsibility for research and model development initiatives, from problem formul antion and data analysis through experimentation, production deployment, and ongoing performance monitoring, driving outcomes with minimal oversight.
• Good communication skills. You can explain technical concepts to engineers and product managers.
• Eager to learn. You are excited to develop deep…