Senior Applied Scientist - Knowledge Graphs & AI

We Love AI Jobs; Hyderabad; Remote; INR 3,500,000 - 5,500,000; Full time

Job summary

Outreach seeks an Associate Applied Scientist to advance its per-tenant knowledge graph and NLP capabilities for contextual sales intelligence. You will design graph representations, extraction pipelines, and reasoning layers, impacting deal risk, next-best-action, and coaching signals.

Applicants should have a PhD or MS with strong ML fundamentals, experience in graph databases, and production-grade Python. Collaborate across engineering and product teams to deploy reliable AI features at scale.

Qualifications

• PhD in CS/NLP/ML or MS with relevant experience.
• Strong ML fundamentals; production-quality Python.
• Experience with graph databases and query languages.

Responsibilities

• Design and implement knowledge graph representations and entity resolution.
• Build information extraction pipelines from calls, emails, and CRM data.
• Develop graph-based features for deal risk, recommendations, and coaching surfaces.
• Train and evaluate graph embeddings for downstream tasks.
• Collaborate with engineers to deploy models and monitor production pipelines.

Skills

Knowledge graphs

Python

Entity resolution

Information extraction

Applied NLP

Graph embeddings

Neo4j

SPARQL

Cypher

Education

PhD in CS/NLP/ML

MS with relevant experience

Tools

Neo4j

SPARQL

Cypher

Job description

# Senior Applied Scientist - Knowledge Graphs & AIOutreachRemote (Hyderabad)Full-timeUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageThis role develops the knowledge graph and applied NLP capabilities behind Outreach’s contextual sales intelligence, including deal-risk signals and next-best-action recommendations. You will build extraction pipelines, graph-based models, and production features while working with senior scientists and cross-functional teams.* Full-time* Fully Remote* PhD or MS plus 2 years of relevant experience* PhD; MS with relevant experience also considered### Skills and expertiseKnowledge graphsPythonEntity resolutionInformation extractionApplied NLPGraph embeddingsNeo4jSPARQL and Cypher### Key responsibilities* Build graph representations and extraction pipelines for information from calls, emails, and CRM data.* Develop graph-based features and models for deal risk, recommendations, and coaching.* Deploy and monitor production models and pipelines with engineering and product teams.## How this job comparesCompared with 410 open senior machine learning jobs on October 10, 2026.Similar open jobs: We Love AI Jobs lists 410 open senior machine learning jobs at 176 companies.Remote work: This job is remote. 90 of the 410 senior machine learning jobs (22%) are remote.AI in this role: High. AI is the core of the work: building, training or deploying AI systems. The role directly builds and evaluates machine-learning models, information-extraction pipelines, and graph-based reasoning features for Outreach’s AI platform. These AI systems are the core of the work.## 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.About the job:• 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 deep expertise under 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:Key Responsibilities:• Knowledge Graph Design & Construction: Design and implement entity resolution and ontology population within established graph schemas. Write and optimize queries 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), including coreference resolution, relation extraction, and event detection. Run experiments to compare approaches and improve accuracy metrics.• 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 Learning: Train and evaluate link prediction and node classification models using established graph embedding methods. Implement evaluation pipelines 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.Our Vision of You:Qualifications:• 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, or conversation AI and dialogue systems. MS + 2 years of relevant experiecne will 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.