Lead Applied Scientist, Document Understanding

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🕒 Outubro 17

🌐 Estados Unidos, Canadá – Remoto

🇺🇸 Estados Unidos – Remoto (EUA) 🇨🇦 Canadá – Remoto

🗽 New York, Minnesota, +1 estados a mais – Remoto

🗽 New York – Remote ❄️ Minnesota – Remote 🤠 Texas – Remote

💵 $147.600 - $274.200 / ano

⏰ Tempo Integral

🟠 Sênior

🧬 Cientista Pesquisador(a)

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Thomson Reuters

SiteLinkedInTodas as Vagas

10.000+ funcionários

Fundada em 2008

💼 Consultoria

⚖️ Jurídico

🛡️ Seguros

Consulting • Legal • Insurance

A Thomson Reuters é uma empresa global de conteúdo e tecnologia que oferece a profissionais soluções confiáveis e precisas nos setores jurídico, fiscal e de contabilidade, comércio internacional e gestão de riscos. A companhia disponibiliza um portfólio que inclui ferramentas de pesquisa jurídica, software de preparação fiscal, soluções de compliance e regulatórias, além de serviços de mídia, como a Reuters, para notícias globais. Com investimentos significativos em IA generativa, a Thomson Reuters segue inovando para ajudar profissionais a otimizar seus fluxos de trabalho e aprimorar a tomada de decisão. Suas plataformas atendem a diversos segmentos — como escritórios de advocacia, corporações, profissionais da área tributária e organizações de mídia —, fornecendo ferramentas essenciais que combinam tecnologia poderosa com conteúdo especializado aprofundado.

Descrição

• Own the design, development, and production deployment of document understanding systems powering Westlaw, PracticalLaw, and CoCounsel • Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents • Build document enrichment systems using legal and customer-defined taxonomies • Develop LLM-based knowledge graph construction pipelines extracting and linking citations, entities, and legal concepts • Lead knowledge distillation efforts to compress large models into latency-constrained, production-ready SLMs • Design component-level and end-to-end evaluation frameworks using expert annotation and synthetic data • Own technical decisions on architecture, chunking strategy, classification approach, and knowledge extraction methods • Partner with engineering on delivery, reliability, and scale across multiple product lines • Provide technical input to senior leadership on AI strategy and roadmap • Mentor applied scientists and ML practitioners

🎯 Requisitos

• PhD in Computer Science, Artificial Intelligence, Natural Language Processing, or a related field • 8+ years of industry experience building and scaling document understanding, information extraction, knowledge graph, or applied NLP solutions • Expertise across the end-to-end ML lifecycle, including model development, evaluation, optimization, distillation, and production deployment • Strong Python programming skills • Hands-on experience with PyTorch, Hugging Face Transformers, and DeepSpeed • Track record of publishing research in leading conferences such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent • Ability to mentor technical talent and drive measurable business impact through production-ready AI systems • Experience translating advanced research into scalable, high-performing products and services • Strong communication and cross-functional collaboration skills • Hands-on production depth in document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods • Hands-on production depth in hierarchical, multi-label document classification with domain-specific and customer-defined schemas • Hands-on production depth in entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text • Hands-on production depth in LLM-based information extraction, few-shot and multi-task learning, and post-training • Hands-on production depth in knowledge distillation, model compression, and SLM deployment under latency constraints • Hands-on production depth in synthetic data generation and annotation workflow design • Hands-on production depth in end-to-end evaluation framework design for document understanding • Preferred: Legal document understanding, legal IE, or legal AI experience • Preferred: Complex document structures, retrieval or QA systems, RAG and agentic workflows, knowledge graph frameworks, AzureML or AWS SageMaker

🏖️ Benefícios

• Flexible work arrangements, including work from anywhere for up to 8 weeks per year • Flexible vacation • Two company-wide Mental Health Days off • Headspace app access • Retirement savings • Tuition reimbursement • Employee incentive programs • Mental, physical, and financial wellbeing resources • Two paid volunteer days off annually • Health, dental, vision, disability, and life insurance programs • Competitive 401(k) plan with company match • Sick and safe paid time off • Paid holidays • Parental leave • Sabbatical leave • Optional hospital, accident and sickness insurance • Optional life and AD&D insurance • Flexible Spending and Health Savings Accounts • Fitness reimbursement • Employee Assistance Program • Group Legal Identity Theft Protection benefit • 529 Plan access • Commuter benefits • Adoption & Surrogacy Assistance • Employee Stock Purchase Plan • Annual Bonus based on enterprise and individual performance (role may be eligible)