AI Researcher (AI-Oriented Knowledge Systems)
Genscript · Piscataway, New Jersey, United States · Posted Jun 24, 2026
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About GenScript
Founded in 2002 in New Jersey, GenScript Biotech Corporation accelerates innovation in biotech and healthcare by providing researchers and companies with the building blocks needed to develop groundbreaking treatments and products. Guided by its mission to Make People and Nature Healthier Through Biotechnology, and its role as a well-recognized biotechnology company, GenScript has a team of approximately 6,165 employees and has served more than 200,000 customers across over 100 countries and regions.
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 (…