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How much do internship knowledge graph jobs pay per hour?

As of Jun 20, 2026, the average hourly pay for internship knowledge graph in the United States is $17.87, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $21.15 per hour, depending on experience, location, and employer.

What is an Internship Knowledge Graph?

An Internship Knowledge Graph is a structured data model that organizes and connects information related to internships, such as companies offering positions, required skills, educational backgrounds, locations, and application deadlines. It uses nodes and relationships to map out how various internship opportunities are related to one another and to relevant entities. This helps students, employers, and educational institutions easily search, analyze, and recommend internships tailored to specific interests and qualifications.

What are the key skills and qualifications needed to thrive as a Knowledge Graph Intern, and why are they important?

To thrive as a Knowledge Graph Intern, you typically need a background in computer science, data science, or a related field, with foundational knowledge in graph theory and semantic web technologies. Familiarity with tools like Neo4j, RDF, SPARQL, and programming languages such as Python or Java is often required. Strong analytical thinking, problem-solving abilities, and communication skills help interns collaborate on complex data modeling tasks and clearly present insights. These skills enable effective contribution to building, optimizing, and maintaining knowledge graph systems that enhance organizational data understanding.

What is the difference between Internship Knowledge Graph vs Data Analyst?

AspectInternship Knowledge GraphData Analyst
Required CredentialsRelevant coursework, basic understanding of knowledge graphsBachelor's degree in data science, statistics, or related field
Work EnvironmentInternship setting, research projects, collaborative teamsCorporate or consulting environments, data-driven decision making
Industry UsageEmerging in AI, semantic web, and knowledge management projectsWidely used across finance, marketing, healthcare, and tech sectors
Search & Comparison IntentUnderstanding entry-level roles involving knowledge graphsAnalyzing data to derive insights and support business strategies

The Internship Knowledge Graph role focuses on foundational understanding and research in knowledge graphs, often suitable for students or entry-level candidates. Data Analysts, however, typically have more advanced data handling skills and work across various industries to interpret data for strategic decisions. While both roles involve data concepts, their scope, environment, and experience levels differ significantly.

What types of projects do interns typically work on in a Knowledge Graph internship?

As a Knowledge Graph intern, you can expect to work on projects involving data modeling, entity extraction, and relationship mapping using large data sets. Interns often collaborate closely with data scientists and software engineers to help build, refine, or maintain knowledge graphs that support enterprise search, recommendation systems, or semantic search features. Typical tasks might include analyzing unstructured data, integrating new data sources, and helping to improve the accuracy and scalability of existing graph-based solutions. This hands-on experience offers valuable exposure to both theory and application in the field of knowledge representation.
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Internship Knowledge Graph jobs near you

2026 Fall Intern, ML/NLP Research

Samsung Research America Internship

Mountain View, CA

Other

Posted 12 days ago

Job description

Lab Summary:AI Research Center (AIC) located in Mountain View, California focuses on research and development which directly impacts future Samsung products reaching hundreds of millions of users worldwide. We are focused on pushing the state-of-the-art and practice in natural language and knowledge intelligence. 

Position Summary: Samsung Research AI center, located in Mountain View, CA, is currently recruiting world-class students who can thrive in a fast-pace, cross team, results-driven environment, with focus on highly visible, challenging, and cross discipline projects. You will be part of an exciting project to build an adaptive, personalized, contextual and secure AI model and system to enable fast, accurate and safe interactions tailored to users' needs on Samsung devices. 

Position Responsibilities: We are looking for Fall Interns (Flexible start date for a 3 month internship between September-December).

  • Develop and implement novel deep learning/reinforcement learning algorithms for natural language processing (text, speech) in various applications 
  • Contribute to the research activities of our team 
  • Generate creative solutions (patents) and publish in top conferences (papers) 

Required Skills: 

  • Current Ph.D. student in CS, EE, or related field 
  • Experience in one or more of the following areas:  
    • Expertise in LLM including model architecture, training/finetuning techniques, retrieval augmented generation (RAG), reasoning and action planning, etc.
    • Experience in planning, tool use, agent AI, and agent memory to develop autonomous systems for decision-making, problem-solving, and adaptability. 
    • Experience in knowledge augmented AI technologies (e.g., language prompt, knowledge graph, neuro-symbolic learning)
    • Experience in conversational AI technologies: natural language processing (e.g., language models, semantic parsing, natural language generation etc.), dialogue (e.g., state tracking, policy learning), and representation learning (embedding, conceptualization, etc.)
    • Experience in multimodal AI technologies for various multimodal applications
    • Experience in on-device AI technologies such as lightweight model architecture design
  • Teamwork and communication skills 
  • Proficiency in a neural network library (e.g., PyTorch, TensorFlow)
  • Track record of research/publications on machine learning and artificial intelligence field (NeurIPS, ICML, ICLR, AAAI, IJCAI, CVPR, ACL, EMNLP, NAACL, TACL, etc.) 

Apply