Llm Knowledge Graph Jobs (NOW HIRING)

Job Title: AI Researcher (AI-Oriented Knowledge Systems)

Location: Piscataway, NJ

The estimated salary range for this role is $85,000 - $145,000 depending on experience. 

Responsibilities

Core Research Directions: Responsible for one or two of the following areas:

Knowledge Extraction & Structuring

• Research techniques for extracting structured knowledge from multi-source heterogeneous data (documents, web pages, databases, conversation logs)
• Design automated pipelines for entity recognition, relation extraction, and event detection
• Develop knowledge quality assessment and cleaning mechanisms to filter noise and conflicting information
• Explore LLM-assisted knowledge extraction methods, balancing automation efficiency with manual validation costs
• Research incremental knowledge extraction strategies to support continuous knowledge base updates and expansion

Knowledge Organization & Representation

• Design knowledge graph schemas and ontologies to build structured frameworks for domain knowledge
• Research Knowledge Embedding techniques to achieve fusion of knowledge and vector spaces
• Develop multi-level knowledge representation systems supporting coarse-to-fine granularity knowledge navigation
• Explore knowledge fusion and alignment techniques to resolve entity disambiguation and conflict resolution from multi-source knowledge
• Research knowledge version management and provenance mechanisms to ensure knowledge traceability

Knowledge Retrieval & Augmentation

• Optimize RAG (Retrieval-Augmented Generation) systems to improve retrieval accuracy and answer quality
• Research hybrid retrieval strategies combining vector search, keyword search, graph traversal, and other approaches
• Develop retrieval re-ranking algorithms to enhance Top-K result relevance
• Design retrieval-generation collaborative optimization mechanisms to reduce hallucinations and erroneous citations
• Explore retrieval feedback learning to continuously optimize retrieval strategies based on user behavior

Knowledge Reasoning & Question Answering

• Research knowledge graph-based reasoning techniques supporting multi-hop reasoning, logical reasoning, and causal reasoning
• Develop Complex QA systems supporting multi-condition and multi-step question answering
• Explore fusion methods combining LLMs with symbolic reasoning, leveraging advantages of both neural and symbolic approaches
• Design interpretability frameworks for reasoning processes, supporting answer provenance and reasoning chain visualization
• Research knowledge gap detection and active learning mechanisms to identify coverage blind spots in the knowledge base

Knowledge Update & Maintenance

• Design knowledge timeliness management mechanisms supporting knowledge expiration detection and automatic updates
• Research knowledge conflict detection and resolution strategies for handling contradictory information fusion
• Develop knowledge base health monitoring systems tracking coverage, accuracy, freshness, and other metrics
• Explore human-feedback-driven knowledge iteration mechanisms
• Research knowledge compression and summarization techniques to optimize storage efficiency and retrieval performance

Job Requirements

Basic Qualifications

• Master's degree or above in Computer Science, Artificial Intelligence, Information Management, or related fields
• 3+ years of AI-related research or development experience with hands-on experience in knowledge graphs, RAG, or QA systems
• Publications in top-tier conferences (ACL, EMNLP, SIGIR, WWW, NeurIPS, etc.) preferred

Technical Skills

Programming & Engineering

• Proficient in Python with expertise in data processing and large-scale text processing techniques
• Familiar with mainstream NLP frameworks (spaCy, NLTK, HuggingFace Transformers, etc.)
• Experience with graph databases (Neo4j, NebulaGraph, JanusGraph, etc.)
• Familiar with vector databases (Milvus, Chroma, Weaviate, FAISS, etc.)

AI Expertise

• Deep understanding of core NLP technologies: entity recognition, relation extraction, text classification, semantic similarity
• Familiar with the full lifecycle of knowledge graph construction and application
• Proficient in RAG technology stack with hands-on experience in retrieval optimization, re-ranking, and answer generation
• Prior experience in vertical domain knowledge system construction and knowledge-driven LLM application deployment (e.g., healthcare, legal, finance, technology) preferred
• Familiar with multi-modal knowledge processing (text + image + table + structured data)

Data Processing

• Familiar with large-scale data processing technologies (Spark, Flink, Dask, etc.)
• Experience in data governance such as data cleaning, deduplication, and standardization preferred
• Familiar with common data formats and protocols (JSON, XML, RDF, OWL, etc.)

Research Capabilities

• Ability to conduct independent technical research, owning the full process from problem definition to solution deployment
• Strong literature review and summarization skills with ability to quickly absorb cutting-edge research findings
• Experimental design and evaluation capabilities, able to design proper comparative and ablation studies

Soft Skills

• Strong interest in the intersection of knowledge engineering and AI, keeping up with latest domain developments
• Excellent communication and collaboration skills, able to work efficiently with engineering and product teams
• Systems thinking ability to approach knowledge system design from an overall architecture perspective

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