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How much do llm knowledge graph jobs pay per year?

As of Jun 7, 2026, the average yearly pay for llm knowledge graph in the United States is $63,311.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,000.00 and $69,500.00 per year, depending on experience, location, and employer.

What is the difference between Llm Knowledge Graph vs Data Scientist?

AspectLlm Knowledge GraphData Scientist
Required CredentialsKnowledge of NLP, graph databases, machine learningStatistics, programming, data analysis
Work EnvironmentResearch labs, AI companies, tech firmsCorporate, consulting, research institutions
Industry UsageAI, knowledge management, semantic webBusiness analytics, predictive modeling

While both roles involve data and machine learning, Llm Knowledge Graph specialists focus on building interconnected knowledge bases using NLP and graph technologies, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap in AI projects but serve different core functions within organizations.

How do knowledge graphs work with LLMs?

Knowledge graphs enhance LLMs by providing structured, interconnected data that improves the models' understanding and reasoning capabilities. Integrating knowledge graphs allows LLMs to access factual information quickly, support more accurate responses, and enable better context management in natural language processing tasks.
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Llm Knowledge Graph jobs near you
Infographic showing various Llm Knowledge Graph job openings in the United States as of May 2026, with employment types broken down into 99% Full Time, and 1% Temporary. Highlights an 54% Physical, 4% Hybrid, and 42% Remote job distribution, with an average salary of $63,311 per year, or $30.4 per hour.
Senior Applied Scientist, Document Understanding

Senior Applied Scientist, Document Understanding

Thomson Reuters

Remote

Full-time

Posted 25 days ago

Thomson Reuters rating

8.9

Company rating: 8.9 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

18th of 425 rated business services

Job description

Job Summary:
Thomson Reuters is a leading provider of trusted content and technology, serving professionals across various sectors. They are seeking a Senior Applied Scientist in Document Understanding to design, build, and deploy document understanding systems that enhance their legal products.
Responsibilities:
• Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents with adjustable granularity across use cases
• Build document enrichment systems that classify documents according to legal and customer-defined taxonomies and extract rich metadata
• Develop LLM-based knowledge graph construction pipelines that extract and link citations, entities, and legal concepts across diverse legal content
• Build scalable synthetic data generation systems for model training, multi-hop query simulation, and hallucination-free answer generation
• Apply knowledge distillation techniques to compress large models into latency-constrained, production-ready SLMs
• Design evaluation frameworks — component-level and end-to-end — using expert annotation and synthetic data
• Drive independent technical decisions on chunking strategy, classification approach, knowledge extraction methods, and multi-document reasoning architecture
• Partner with engineering on delivery, reliability, and scale across multiple product lines
• Contribute to published research at venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, and KDD, and to intellectual property
Qualifications:
Required:
• PhD or Master's in Computer Science, AI, NLP, or a related field
• 5+ years of post-degree industry experience shipping document understanding, information extraction, or knowledge graph systems into production — not research-only experience
• Publications at ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent
• Experience leading through influence in an applied research setting
• Production Python and experience with PyTorch, Hugging Face Transformers, and DeepSpeed
• Document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods
• Hierarchical, multi-label document classification with domain-specific and customer-defined schemas
• Entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text
• LLM-based information extraction, few-shot and multi-task learning, and post-training
• Knowledge distillation, model compression, and SLM deployment under latency constraints
• Synthetic data generation for NLP: query-answer generation with verification and scalable data augmentation
• Annotation workflow design and evaluation framework development for document understanding tasks
Preferred:
• Legal document understanding, legal information extraction, or legal AI applications
• Complex document structures common in legal content: nested hierarchies, cross-references, non-uniform formatting, and embedded elements
• Retrieval, QA, or analysis systems over large document collections
• Knowledge graph frameworks for legal or enterprise applications
• RAG and agentic workflows for enterprise knowledge systems
• AzureML or AWS SageMaker
Company:
Thomson Reuters delivers critical information from the financial, legal, accounting, intellectual property, science, and media markets. Founded in 2008, the company is headquartered in Toronto, CAN, with a team of 10001+ employees. The company is currently Late Stage.

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