Research Engineer – Neuro-Symbolic AI (Knowledge Graphs) & Multimodal Assistant Systems
Core
Conduct research integrating formal knowledge representations (knowledge graphs) with machine learning and deep learning to build explainable, trustworthy multimodal AI assistants.
Role type
Research Engineer (Neuro-Symbolic AI & Knowledge Graphs)
Builds
Knowledge-driven AI systems, multimodal data pipelines, semantic assets (ontologies, knowledge graphs), and production-ready research prototypes.
Domain
Artificial Intelligence, Neuro-Symbolic AI, Knowledge Graphs, Multimodal Systems
Deliverable
production ML models | research | product features
Required skills
Neuro-Symbolic AI architectures, Multi-modal data engineering, NLP frameworks (Transformers, spaCy), Knowledge extraction (NER, RE), Graph libraries (RDFlib, PyTorch Geometric), Graph visualization (Gephi, D3.js)
Preferred skills
Ontology design & reasoning, Hybrid reasoning engines, Enterprise knowledge graph tech (Neo4J, Stardog), Symbolic query languages (SPARQL, Prolog), LLM/VLM fine-tuning & RAG, Graph embeddings & GNNs
Responsibilities
Design multimodal data pipelines; Integrate Neuro-Symbolic reasoning with LLMs; Design and evolve semantic assets; Prototype and validate research concepts; Collaborate with cross-disciplinary teams; Publish research findings and file patents.
Seniority
Senior, hands-on IC