Thomson Reuters Enterprise Centre GmbH
Senior Applied Scientist, Search - NLP/GenAI(m/w/x)
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THThomson Reuters Enterprise Centre GmbH
Lead Applied Scientist, Search - NLP/GenAI(m/w/x)
Vollzeitmit HomeofficeSenior
AI/ML
Nejo KI-Zusammenfassung
Leading AI solutions for document understanding, including LLM-based knowledge graphs and synthetic data generation. PhD or Master's with deep learning and NLP experience required. Flexible hybrid work, 8 weeks work-from-anywhere, and Headspace access.
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Anforderungen
- PhD in Computer Science, AI, NLP, or related field, or Master's with equivalent research/industry experience
- Hands-on experience building/deploying document understanding systems, information extraction, or knowledge graph construction using deep learning, LLMs, NLP
- Ability to translate complex document understanding problems into innovative AI applications balancing accuracy and efficiency
- Ability to provide technical leadership, mentor team members, and influence without formal authority
- Strong programming skills (e.g., Python) and experience with deep learning frameworks (e.g., PyTorch, Hugging Face Transformers, DeepSpeed)
- Publications at relevant venues (ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD)
- Deep understanding of document understanding fundamentals (layout analysis, semantic chunking, classification, domain-specific schemas)
- Expertise in knowledge extraction and knowledge graph construction (entity recognition/linking, relation extraction, citation parsing, graph building)
- Expertise in LLM-based information extraction, few-shot/multi-task learning, post-training, knowledge distillation
- Solid understanding of synthetic data generation techniques for NLP (query-answer generation, scalable data augmentation)
- Solid understanding of efficiency optimization (knowledge distillation, model compression, SLM-based solutions)
- Solid understanding of DL/ML approaches for NLP tasks
- Experience designing annotation workflows, creating labeled datasets, and developing evaluation frameworks for document understanding
Aufgaben
- Lead the design, build, test, and deployment of end-to-end AI solutions for document understanding
- Direct large-scale projects including advanced semantic chunking models
- Direct document enrichment systems with legal and customer-defined taxonomies
- Direct LLM-based knowledge graph construction pipelines
- Direct scalable synthetic data generation systems
- Serve as the technical lead and primary point of reference
- Ensure full accountability for all research deliverables
- Partner with engineering to guarantee software delivery and reliability at scale
- Design comprehensive evaluation strategies for component-level and end-to-end quality
- Apply robust training methodologies balancing performance with latency
- Lead knowledge distillation initiatives to compress large models
- Maintain scientific and technical expertise through product deliverables
- Inform Labs shared capabilities and research themes
- Independently determine appropriate architectures for document understanding challenges
- Make critical technical decisions on semantic chunking strategies
- Make critical technical decisions on document classification approaches
- Make critical technical decisions on LLM-based knowledge extraction methods
- Make critical technical decisions on multi-document reasoning architectures
- Provide input to stakeholders on long-term AI strategy
- Develop in-depth knowledge of TR customers and data infrastructure
- Partner closely with Engineering and Product teams to translate challenges into solutions
- Engage stakeholders to understand use case requirements
- Shape objectives aligning document understanding with business needs
- Mentor and coach team members with varied ML/NLP abilities
- Build technical capability across the organization
Berufserfahrung
ca. 4 - 6 Jahre
Ausbildung
Master-Abschluss
Sprachen
Englisch – verhandlungssicher
Tools & Technologien
PythonPyTorchHugging Face TransformersDeepSpeedDeep LearningLLMsNLP
Benefits
Flexibles Arbeiten
- Flexible hybrid working environment
Workation & Sabbatical
- Work from anywhere up to 8 weeks per year
Mehr Urlaubstage
- Flexible vacation
Mentale Gesundheitsförderung
- Two company-wide Mental Health Days off
- Headspace app access
- Mental wellbeing resources
Betriebliche Altersvorsorge
- Retirement savings
Sonstige Zulagen
- Tuition reimbursement
- Financial wellbeing resources
Boni & Prämien
- Employee incentive programs
Gesundheits- & Fitnessangebote
- Physical wellbeing resources
Gemeinnützige Ausrichtung
- Two paid volunteer days off annually
- Pro-bono consulting project opportunities
Fokus auf Nachhaltigkeit
- ESG initiative opportunities
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