AI Researcher (AI-Oriented Knowledge Systems)

BioSpace · Piscataway, NJ · 5 days ago

Engineering$85k–$145k/yrFull-time

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

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

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

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

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

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
Publishations in top-tier conferences (ACL, EMNLP, SIGIR, WWW, NeurIPS, etc.) preferred

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.)

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)

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.)

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

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

Medical, dental, and vision insurance
401(k) retirement plan with a company match that vests fully on day one
Paid parental leave after just three (3) months of employment
Paid time off policy that includes vacation time, personal time, sick time, floating holidays, and company holidays
Flexible spending and health savings accounts
Life and AD&D insurance
Short- and long-term disability coverage
Legal assistance
Supplemental plans such as pet, critical illness, accident, and hospital indemnity insurance
Commuter benefits
Well-being initiatives
Peer-to-peer recognition programs

Full time

$85,000 - $145,000 depending on experience

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