NLP Engineer _ Machine Learning Engineer|5+ years| Remote
Job description:
NLP Engineer / Machine Learning Engineer Document Understanding & Knowledge Graphs
ā¢ Overview Were looking for a hands-on NLP/ML engineer to lead the development of an intelligent document understanding pipeline for extracting structured data from complex, unstructured RFQ documents (40100+ pages, in German and English).
ā¢ You will be responsible for building scalable systems that combine document parsing, layout analysis, entity extraction, and knowledge graph construction ultimately feeding downstream (e.g. Analytics and LLM applications.)
ā¢ Key Responsibilities - - - - - -
ā¢ Design and implement document hierarchy and section segmentation pipelines using layout-aware models (e.g., DocLayout-YOLO, LayoutLM, Donut).
ā¢ Build multilingual entity recognition and relation extraction systems across both English and German texts.
ā¢ Use tools like NLTK, transformers, and spaCy to develop custom tokenization, parsing, and information extraction logic.
ā¢ Construct and maintain knowledge graphs representing semantic relationships between extracted elements using graph data structures and graph databases (e.g. Neo4j) Integrate outputs into structured LLM-friendly formats (e.g., JSON, Mark Down) for downstream extraction of building material elements.
ā¢ Collaborate with product and domain experts to align on information schema, ontology, and validation methods. What Were Looking For - - - -
ā¢ Strong experience in NLP, document understanding, and information extraction from unstructured/multilingual documents.
ā¢ Proficiency in Python, with experience using libraries such as transformers, spaCy, and NLTK. Hands-on experience with layout-aware models like DocLayout-YOLO, LayoutLM, Donut, or similar.
ā¢ Familiarity with knowledge graphs and graph databases such as Neo4j, RDF
NLP Engineer / Machine Learning Engineer Document Understanding & Knowledge Graphs
ā¢ Overview Were looking for a hands-on NLP/ML engineer to lead the development of an intelligent document understanding pipeline for extracting structured data from complex, unstructured RFQ documents (40100+ pages, in German and English).
ā¢ You will be responsible for building scalable systems that combine document parsing, layout analysis, entity extraction, and knowledge graph construction ultimately feeding downstream (e.g. Analytics and LLM applications.)
ā¢ Key Responsibilities - - - - - -
ā¢ Design and implement document hierarchy and section segmentation pipelines using layout-aware models (e.g., DocLayout-YOLO, LayoutLM, Donut).
ā¢ Build multilingual entity recognition and relation extraction systems across both English and German texts.
ā¢ Use tools like NLTK, transformers, and spaCy to develop custom tokenization, parsing, and information extraction logic.
ā¢ Construct and maintain knowledge graphs representing semantic relationships between extracted elements using graph data structures and graph databases (e.g. Neo4j) Integrate outputs into structured LLM-friendly formats (e.g., JSON, Mark Down) for downstream extraction of building material elements.
ā¢ Collaborate with product and domain experts to align on information schema, ontology, and validation methods. What Were Looking For - - - -
ā¢ Strong experience in NLP, document understanding, and information extraction from unstructured/multilingual documents.
ā¢ Proficiency in Python, with experience using libraries such as transformers, spaCy, and NLTK. Hands-on experience with layout-aware models like DocLayout-YOLO, LayoutLM, Donut, or similar.
ā¢ Familiarity with knowledge graphs and graph databases such as Neo4j, RDF