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Local POI Knowledge Graph

Mountain View, CA

$142K/yr

You will build our local knowledge graph: the POI and places-data systems, entity resolution, and ontologies that turn fragmented, multi-source business data into a clean, queryable graph. If you ...

Trust IT LLC

Lead Knowledge Graph Engineer

Collegeville, PA · On-site

$101K - $133K/yr

We are seeking a Lead Knowledge Graph Engineer for a high-priority, 12+ month contract on-site in Upper Providence, PA (12 days/month hybrid schedule). In this role, you will act as the technical ...

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How much do knowledge graph jobs pay per hour?

As of Jul 23, 2026, the average hourly pay for knowledge graph in the United States is $31.03, according to ZipRecruiter salary data. Most workers in this role earn between $15.87 and $25.96 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Knowledge Graph position, and why are they important?

To thrive as a Knowledge Graph Engineer, you need strong skills in semantic web technologies, ontology modeling, and data integration, typically supported by a background in computer science or data science. Familiarity with tools like RDF, SPARQL, OWL, and knowledge graph platforms (e.g., Neo4j, GraphDB) is common, and certifications in data engineering or semantic technologies are beneficial. Effective communication, problem-solving abilities, and cross-functional collaboration are valuable soft skills in this field. These competencies are crucial for designing, implementing, and maintaining knowledge graphs that enable advanced data discovery and insights for organizations.

Is ML a high paying job?

Machine Learning (ML) roles, including positions like ML engineer or data scientist, are generally well-paid due to the specialized skills required, such as programming, statistics, and knowledge of algorithms. Salaries tend to be higher than average in tech hubs and often increase with experience, certifications, and proficiency in tools like Python, TensorFlow, or PyTorch.

What is a knowledge graph job description?

A knowledge graph job description typically involves designing, developing, and maintaining knowledge graphs that organize and connect data for improved search, reasoning, and data integration. The role often requires skills in data modeling, graph databases like Neo4j, and understanding of semantic technologies such as RDF and OWL. Professionals in this field may work with data scientists, software engineers, and domain experts to ensure accurate and efficient knowledge representation.

What is a Knowledge Graph job?

A Knowledge Graph job typically involves designing, building, and maintaining structured representations of data that map relationships between entities. Professionals in this role work with technologies like RDF, SPARQL, ontologies, and graph databases to enhance data integration, retrieval, and reasoning. These jobs are common in AI, search, and data science fields, helping organizations improve knowledge discovery and decision-making.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as AI research director, senior machine learning engineer, or AI product executive, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership, strategic planning, and expertise in tools like TensorFlow or PyTorch, with compensation reflecting experience and impact. Such salaries are rare and generally found in top tech companies or specialized AI firms.

What engineer makes $500,000 a year?

Senior data engineers or machine learning engineers working in high-demand industries such as technology, finance, or AI can earn salaries around $500,000 annually, especially with extensive experience, advanced skills in big data tools, and relevant certifications. Compensation varies based on location, company size, and individual expertise.

What are some typical daily responsibilities of a Knowledge Graph Engineer?

As a Knowledge Graph Engineer, your typical day involves designing and developing ontologies, integrating diverse data sources, and implementing graph-based data models to enhance information accessibility. You may work closely with data scientists, software developers, and business analysts to gather requirements and translate them into scalable knowledge graph solutions. Regular tasks include writing SPARQL queries, performing data mapping, maintaining documentation, and troubleshooting graph data issues. Collaboration and ongoing learning are integral as this field rapidly evolves with new tools and best practices.

More about Knowledge Graph jobs
What cities are hiring for Knowledge Graph jobs? Cities with the most Knowledge Graph job openings:
What are the most commonly searched types of Knowledge Graph jobs? The most popular types of Knowledge Graph jobs are:
What states have the most Knowledge Graph jobs? States with the most job openings for Knowledge Graph jobs include:
What job categories do people searching Knowledge Graph jobs look for? The top searched job categories for Knowledge Graph jobs are:
Knowledge Graph jobs near you
Infographic showing various Knowledge Graph job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 61% Full Time, 33% Part Time, 3% Contract, and 2% Summer. Highlights an 55% Physical, 3% Hybrid, and 42% Remote job distribution, with an average salary of $64,550 per year, or $31 per hour.
Senior Ontologist - Knowledge Graph & Identity

Senior Ontologist - Knowledge Graph & Identity

Samba

San Francisco, CA • On-site, Remote

$144K - $190K/yr

Full-time

Posted 16 days ago

Job description

Samba is a media intelligence company. We know what the world is watching, reading, and thinking about - in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it's the most interesting data asset on the planet, because it's the most culturally relevant. 

As Senior Ontologist on Samba TV's Knowledge Graph & Identity team, you will own the design, development, and governance of the semantic data models and ontological frameworks that sit at the foundation of Samba's knowledge graph. You are the domain authority for how Samba represents and relates the entities that matter most to our business - and you ensure that representation is rigorous, scalable, and aligned with industry standards.

This is a hands-on technical role. You will spend the majority of your time designing ontologies, writing SPARQL, building knowledge graph pipelines, and working closely with data engineering and data science peers to put your models into production. You bring enough breadth in ML and AI to leverage embedding-based and LLM-augmented approaches where they strengthen the graph, and you contribute meaningfully to entity resolution and identity linking work that depends on the semantic layer you define.

This role reports to the Data Science Manager, Knowledge Graph & Identity.

What You'll Do:

Ontology Design & Governance

  • Own the end-to-end design, development, and versioning of Samba TV's core ontologies in RDF/RDFS/OWL - defining entity classes, properties, hierarchies, and constraints that accurately model Samba's data domain at scale

  • Author and maintain SHACL shapes for post-load graph validation, consistency checking, and data quality enforcement

  • Define and document derived-attribute schemas - genre affinity, brand affinity, topic affinity, lifecycle signals, and viewing summaries - and own the logical definitions that govern how raw events become durable graph attributes

  • Establish ontology design standards, change management processes, and versioning practices; evaluate alignment with W3C standards and relevant industry schemas (Schema.org, EIDR, DDEX, W3C PROV)

  • Lead ontology design reviews with product, data engineering, and data science stakeholders - articulating trade-offs between expressivity, scalability, and query performance clearly

Event-to-Ontology Derivation

  • Define the aggregation and scoring logic that transforms raw TV viewership and web activity events into the durable affinities, summaries, and inferred signals that live in the graph

  • Co-own derivation pipeline design with data engineering - specifying transformation logic, intermediate schemas, and validation checkpoints for Databricks/Spark pipelines that feed the materialized graph substrate

  • Reason carefully about what belongs in the graph vs. what should remain virtualized in the data lake - balancing query performance against storage and refresh cost

Knowledge Graph Development & AI Integration

  • Build and maintain production-quality knowledge graph pipelines in Python and SPARQL - well-tested, documented, and scalable to Samba's data volumes

  • Design and implement entity resolution and record linkage pipelines that map real-world entities (content titles, devices, audiences, advertisers) to canonical knowledge graph nodes

  • Develop enrichment workflows that integrate third-party data sources (metadata providers, identity vendors, web sources) into Samba's knowledge graph in a consistent, governed way

  • Apply embedding-based and LLM-augmented approaches to ontology mapping, entity disambiguation, and semantic similarity problems

  • Support content and semantic embedding pipelines that feed into the vector store and underpin GraphRAG-based AI solutions

Cross-functional Collaboration & Mentorship

  • Partner with data engineering and platform teams to ensure the knowledge graph is integrated, queryable, and production-ready at scale

  • Collaborate with product to translate business requirements into ontological and graph data model decisions

  • Formally mentor Ontology Engineers and junior data scientists on semantic modeling, SHACL design patterns, and graph best practices

  • Lead internal technical talks and workshops on ontology, knowledge graph, and semantic web topics

Who You Are:

Must-Haves

  • 5-8 years of hands-on experience in ontology engineering, semantic data modeling, or knowledge graph development - with a demonstrable track record of production ontologies at scale

  • Deep expertise in W3C semantic web standards: RDF, RDFS, OWL, SPARQL 1.1, and SHACL - with hands-on experience building and validating graph schemas in a production triplestore (Amazon Neptune, Stardog, GraphDB, Jena, or equivalent)

  • Strong Python - production-quality, well-tested code; comfortable building data pipelines and graph processing workflows

  • First-principles understanding of description logics, ontology design patterns, and the practical trade-offs between OWL expressivity and triplestore scalability

  • Hands-on experience with entity resolution, record linkage, or deduplication at scale - mapping messy, multi-source real-world data to clean ontological representations

  • Bachelor's degree required in Computer Science, Information Science, Computational Linguistics, Mathematics, or a related field; Master's or PhD strongly preferred

  • Strong communicator - able to defend ontological modeling decisions in design reviews and explain trade-offs to non-specialist stakeholders

Strongly Preferred

  • Hands-on experience with Amazon Neptune or Stardog - including data virtualization (Neptune Orion or Stardog Virtual Graphs) over data lake sources

  • Experience designing aggregation and derivation logic that converts raw behavioral event data into durable, graph-resident derived attributes

  • Domain knowledge in media, entertainment, or ad tech - TV viewership (ACR/STB), digital audience modeling (device graphs, identity resolution), or ad exposure data

  • Familiarity with industry content and identity schemas: EIDR, Schema.org VideoObject, DDEX, or equivalent

  • Experience with embedding models, vector databases (Milvus, Pinecone, Weaviate), and GraphRAG architectures (LangChain/LlamaIndex)

  • Familiarity with GNN-based approaches to knowledge graph reasoning or entity resolution a plus

  • Working knowledge of PySpark and Databricks for large-scale transformation pipelines

$180,000 - $230,000 a year
Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.  We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.
 
Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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