About the roleWe’re 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). You will build scalable systems combining document parsing, layout analysis, entity extraction, and knowledge graph construction—ultimately feeding downstream (e.g., Analytics and LLM applications).Key ResponsibilitiesDesign and implement document hierarchy and section segmentation pipelines using layout-aware models (e.g., DocLayout-YOLO, LayoutLM, Donut).Build multilingual entity recognition and relation extraction systems across both English and German texts.Use tools like NLTK, transformers, and spaCy to develop custom tokenization, parsing, and information extraction logic.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, Markdown) for downstream extraction of building material elements.Collaborate with product and domain experts to align on information schema, ontology, and validation methods.What we’re looking forStrong experience in NLP, document understanding, and information extraction from unstructured/multilingual documents.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.Familiarity with knowledge graphs and graph databases such as Neo4j, RDF.

NLP Engineer _ Machine Learning Engineer

Emma

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