AI/ML Data Knowledge Graph Engineer

Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share.

The intelligence a community needs is already inside it. Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves.
This role builds the structured knowledge that collective intelligence reasons over. You own the KO (knowledge object) graph: the layer that turns a community’s scattered expertise into connected, queryable knowledge the COGENT architecture can use.
You work where messy real-world data becomes trustworthy structure: ingesting, resolving, connecting, and modeling knowledge so reasoning has something solid to stand on.
You partner closely with neuro-symbolic AI and applied AI, and you are the reason the platform can answer questions that span a community’s knowledge instead of isolated documents.
Language models are fluent, but fluency is not knowledge. To reason over a community’s expertise with rigor, the platform needs that expertise structured, connected, and trustworthy, not just retrieved as text.
Building a knowledge graph from real, fragmented sources is hard: entities to resolve, relationships to infer, quality to enforce, and provenance to preserve. The graph is only as good as the engineering behind it.
The AI/ML Data and KO Graph Engineer builds that foundation. You turn scattered knowledge into a graph the COGENT architecture can reason over, so members get answers grounded in their community’s real expertise.
Knowledge graph engineering

Build and maintain the KO graph that structures a community’s knowledge for reasoning.
Build pipelines that extract knowledge from documents, systems, and community sources into the graph.
Turn unstructured and semi-structured content into structured knowledge objects.
Keep the graph current as a community’s knowledge changes.
Entity resolution and quality

Enforce quality so members can trust what the graph tells them.
Build the structure that lets the platform show its work and earn member trust.
Protect sensitive community knowledge with correct access and governance.
Serving knowledge to COGENT

Partner with neuro-symbolic and applied AI to serve the graph into reasoning and retrieval.
Data pipelines and platform

Build the data pipelines and platform the knowledge layer depends on.
Instrument the pipelines so quality and freshness can be measured.
Evaluation of knowledge quality

Measure the quality, coverage, and freshness of the graph against what communities need.
AI-augmented ways of working
You use AI to build the knowledge layer, from extraction and entity resolution to schema suggestions, while you own the correctness, structure, and trustworthiness of the graph.
The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. neuro-symbolic AI owns the reasoning.
You own structure, quality, provenance, and how knowledge serves reasoning.
The graph is a production foundation members’ trust depends on.
A community’s scattered expertise becomes a connected, queryable graph.
Quality and provenance let members trust and trace what the platform tells them.
The graph keeps pace with a community’s changing knowledge.
New sources come online faster because ingestion is reusable.
Measured quality.

Coverage, quality, and freshness are measured and improving.
Five or more years in data engineering, knowledge graph engineering, or a related field.
Hands-on experience building and operating knowledge graphs or graph databases.
Strong data pipeline engineering, including ingestion and transformation.
Experience with entity resolution, deduplication, and data quality.
Strong Python and SQL, plus graph query languages.
Care for provenance, trust, and protection of sensitive data.
Experience serving graphs into retrieval or reasoning systems.
Familiarity with neuro-symbolic AI and how structure supports reasoning.
Experience integrating CRM, AMS, or knowledge-base sources.
Domain understanding of knowledge-intensive or professional communities.
You name the real problem in the data before reaching for a structure.
You care about quality, provenance, and trust as much as coverage.
You build pipelines others can run and extend.
Knowledge graph and ontology engineering.
Ingestion, extraction, and transformation pipelines.
Entity resolution, deduplication, and data quality.
Serving graphs into retrieval and reasoning.
Evaluation of knowledge quality and coverage.
Graph databases (for example Neo4j-class systems) and graph query languages (Cypher, SPARQL, or GQL).
Data pipeline and orchestration tools.
Entity resolution and data-quality tooling.
Vector databases and embedding models for hybrid retrieval.
Python and SQL as primary languages.
Cloud data platforms and storage.
Building and serving the KO graph into the COGENT architecture and MINERVA.
Prior data or knowledge graph engineering at a software or AI company.
Experience building knowledge structures from messy, real-world sources.
A track record of production data systems with quality and provenance.
You work most closely with Neuro-Symbolic AI, Applied AI, Data Engineering, and Platform Engineering. We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. Sapience AI is an equal opportunity employer. We are committed to a workplace where everyone, regardless of background, has a voice in building what comes next.
Base Salary: $204,000 - $216,000 + early stage equity
Sapience AI is an equal opportunity employer. We do not discriminate on the basis of gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. If you need an accommodation to complete our application process, let your recruiter know.