Senior Applied Scientist - Knowledge Graphs & AI

Outreach, founded in 2014, is a leading agentic AI platform that empowers revenue teams by automating workflows and enhancing strategic decision-making. Utilizing agentic AI, conversation intelligence, and assistive AI, Outreach supports various revenue functions such as prospecting, deal acceleration, retention, and forecasting. Trusted by industry leaders like Databricks, SAP, Siemens, and Verizon, the platform delivers connected account visibility, performance insights, and improved forecasting accuracy across go-to-market teams.

We are looking for an Associate Applied Scientist to join our AI platform team. This role involves applying machine learning techniques to knowledge graphs and reasoning systems at scale, contributing to the development of Outreach’s per-tenant knowledge graph. This graph models the complexity of each customer’s sales environment—including accounts, deals, contacts, rep behaviors, competitive landscape, and signals from calls, emails, and CRM activity—to enable contextual reasoning and generate actionable insights such as next-best-action recommendations, deal risk signals, and coaching suggestions. The position requires designing representations, extraction pipelines, and reasoning layers while collaborating closely with engineering and product teams to deliver scalable, reliable AI solutions that directly impact revenue outcomes. This opportunity is ideal for candidates with strong machine learning fundamentals who want to deepen their expertise in knowledge graphs and applied NLP within a dynamic product environment.

Key Responsibilities:
- Design and implement entity resolution and ontology population within established graph schemas, optimizing queries for graph traversal and feature extraction while ensuring data quality.
- Develop pipelines to extract structured knowledge from unstructured conversational and document data, including coreference resolution, relation extraction, and event detection; conduct experiments to improve accuracy.
- Implement graph traversal logic and feature queries that support downstream scoring signals for deal risk, next-best-action, and coaching recommendations.
- Train and evaluate link prediction and node classification models using graph embedding methods; maintain evaluation pipelines and monitor model performance over time.
- Translate sales concepts such as deal stages, buyer engagement, rep behaviors, and account health into graph nodes and relationships; contribute to ontology design and documentation.
- Collaborate with software engineers to deploy models and pipelines into production, write clean and tested code, monitor system health, respond to incidents, and participate in code reviews and design discussions.

Required Qualifications:
- PhD in Computer Science, NLP, Machine Learning, or related fields focused on knowledge representation, information extraction, graph neural networks, recommendation systems, or conversational AI; or MS with 2+ years of relevant experience.
- Strong engineering skills with the ability to write production-quality, clean, and tested Python code; experience with graph databases or query languages such as Neo4j, SPARQL, or Cypher.
- Proven experience building and evaluating machine learning models, including training, performance measurement, and iterative improvement.
- Demonstrated ability to develop research prototypes into reliable production systems, understanding operational challenges such as monitoring, data drift, and latency.
- Strong ownership mindset with end-to-end responsibility for research and model development, from problem formulation to deployment and ongoing monitoring.
- Effective communication skills to explain technical concepts to engineers and product managers.
- Enthusiasm for learning and developing deep expertise in knowledge graphs and applied NLP under senior mentorship.

Preferred Qualifications and Benefits:
- Hands-on experience applying knowledge graphs or graph-based learning methods in production environments.
- Strong fundamentals in at least two areas: knowledge graph construction, information extraction, graph neural networks, or recommender systems.
- Experience working with large-scale unstructured text data such as conversational transcripts or emails.
- Familiarity with probabilistic graphical models, conversational AI, or sales/revenue domain data.
- Published research in top-tier venues.
- Competitive salary with 25 days of annual vacation plus sick and casual leave.
- Group medical coverage for employees and eligible family members, including outpatient benefits.
- Life and personal accident insurance at three times annual CTC.
- Generous parental leave policies.
- Employee stock ownership through RSU programs.
- Inclusive diversity programs supporting multiple employee resource groups.
- Employee referral bonuses and engaging company outings.

Outreach is an equal opportunity employer committed to building diverse teams and welcomes applicants from all backgrounds. Candidates are encouraged to apply even if they do not meet every qualification listed. Our hiring process may involve AI-assisted tools to support recruitment, but final decisions are made by humans.