Lead Applied Scientist, NLP/GenAI(m/w/x)

Requirements

• PhD in Computer Science, AI, NLP, or related field, or Master's with equivalent research/industry experience
• 7+ years hands-on experience building/deploying document understanding systems, information extraction pipelines, or knowledge graph construction
• Ability to translate complex document understanding problems into AI applications
• Technical leadership, mentoring, and influence
• Strong Python programming and deep learning frameworks experience
• Publications at relevant venues (ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD)
• Deep understanding of document understanding fundamentals
• Expertise in knowledge extraction and knowledge graph construction
• Expertise in LLM-based information extraction, few-shot/multi-task learning, post-training, knowledge distillation
• Solid understanding of synthetic data generation for NLP
• Solid understanding of efficiency optimization (knowledge distillation, model compression, SLM solutions)
• Solid understanding of DL/ML approaches for NLP tasks
• Experience designing annotation workflows, creating labeled datasets, evaluation frameworks
• Prior work on legal document understanding, information extraction, knowledge representation, or legal AI applications
• Prior work handling complex legal document structures
• Experience building systems for analysis, question answering, or retrieval across large document collections
• Experience with knowledge graph frameworks and methodologies for legal/enterprise applications
• Understanding of RAG and agentic workflows for enterprise knowledge
• Experience working with AzureML or AWS SageMaker

Tasks

• Lead AI solution development for legal document understanding
• Oversee advanced semantic chunking for legal documents
• Oversee document enrichment systems with taxonomies
• Oversee LLM-based knowledge graph construction pipelines
• Oversee scalable synthetic data generation systems
• Act as the technical lead
• Ensure accountability for research deliverables
• Partner with engineering for reliable software delivery at scale
• Design comprehensive evaluation strategies using expert annotation
• Apply robust training methodologies balancing performance and latency
• Lead knowledge distillation for production-ready SLMs
• Maintain scientific and technical expertise
• Inform Labs capabilities and research with novel approaches
• Determine architectures balancing accuracy, efficiency, and scalability
• Decide on semantic chunking strategies
• Decide on document classification approaches
• Decide on LLM-based knowledge extraction methods
• Decide on multi-document reasoning architectures
• Advise stakeholders on long-term AI strategy
• Develop customer and data knowledge for technical roadmaps
• Collaborate with Engineering and Product on scalable solutions
• Engage stakeholders to align capabilities with business needs
• Mentor and coach team members
• Build technical capability across the organization

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