- Thomson Reuters Labs
Thomson Reuters Labs
Thomson Reuters is a content-driven technology company with over a century of experience curating and classifying data and supporting professionals in complex domains that move society forward.
Embedded in this legacy is our team of AI-focused engineers, research and data scientists, and designers who work with some of the most comprehensive and richly enhanced legal, tax, and other professional datasets in the world.
Join us in building the next generation of AI-enabled solutions for legal, tax & accounting, risk, fraud and compliance, and media.
“My work provides me with opportunities to experiment, learn, and make meaningful contributions and connections.”
Sr. DevOps Engineer
“You get appreciated for everything you do and bring to the table. It’s a fantastic combination of work & personal life balance.”
Reasons our team loves it here:
- Access to world-leading data and enterprise expertise
- Diverse team and inclusive culture of world-class talent
- Talented colleagues to learn and collaborate with
- Opportunity for mentorship and career growth
- World-class wellbeing benefits and resources
- Access to industry leading learning tools and resources
About the Labs
Thomson Reuters Labs is the dedicated applied research division of Thomson Reuters. We are focused on the research, development, and application of AI and emerging trends in technologies. Working collaboratively with our stakeholders, we experiment, prototype, test, and deliver ideas in the pursuit of smarter and more valuable tools for our customers.
We have worked in AI for decades. The groundwork for much of this work began in the 1970s with Westlaw – one of the first online legal research services.
We are committed to a diverse program of machine learning research, from efficient neural training to learning with less supervision, human-centered AI and many other core capabilities with business-critical impact.
We have drafted a set of AI Principles to promote trustworthiness in our design, development and deployment of AI.
Norkute, M., Herger, N., Michalak, L., Mulder, A., and Gao, S. (2021). Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, CHI EA ’21, New York, NY, USA. Association for Computing Machinery.
Thomas, M., Vacek, T., Shuai, X., Liao, W., Sanchez, G., Sethia, P., Teo, D., Madan, K., and Custis, T. (2020). Quick check: A legal research recommendation system. In Proceedings of the 2020 Natural Legal Language Processing (NLLP) Workshop. pages 57-60.
Sanchez, G. (2019). Sentence boundary detection in legal text. In Proceedings of the Natural Legal Language Processing Workshop 2019, pages 31–38, Minneapolis, Minnesota. Association for Computational Linguistics.
Chinnappa, D. and Blanco, E. (2021). Extracting possessions from text: Experiments and error analysis. Natural Language Engineering, pages 1–22.
Song, D., Vold, A., Madan, K., and Schilder, F. (2021). Multi-label legal document classification: A deep learning-based approach with label-attention and domain-specific pre-training. Information Systems, page 101718.
Westlaw: Narrated by Uwais Iqbal, a labs Data Scientist, learn about our Legaltech Data Challenge using Westlaw case data.
Westlaw Edge: Learn about our work with Quick Check on Westlaw Edge, from CJ Lechtenberg, Director of Westlaw Product Management and Merine Thomas, Associate Architect working in Thomson Reuters Labs.
Identify and integrate the end-user • Apply a design thinking approach
Articulate the hypothesis • Understand what success looks like
Prototype and test for viability • Iterate through to production
Working in AI for Decades • Guided by Our AI Ethics Principles • Flexible Work Policy • Competitive Benefits Package
Labs disciplines and skills
We take a cross-functional approach to building solutions by having research and data scientists, engineers and designers involved at an early stage in our projects. By having embedded teams we can move quickly, identify obstacles and opportunities early, and ensure a diversity of perspectives on problem solving.
Machine Learning • Search + Recommendation • Natural Language Processing
Software Engineering • Machine Learning + Data Engineering • Public Cloud
User Experience Research • User Experience Design • User Interface Design
Sr. Research Scientist
“In Thomson Reuters there are ample opportunities to make an impact in the form of patents, scientific publications and products. ”
VP, Research and Data Science
“We put our intellectual curiosity to work in solving our customers’ problems. With high activity in academic and industry communities, we seek to empower our teams toward personal and career development.”
Our team expands across the globe, operating across multiple sectors and geographies. Join a diverse team and inclusive culture of world-class talent.
Apply for a role at Thomson Reuters Labs today
We seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law.