Transform information retrieval systems as an Applied Scientist at Thomson Reuters. Engage in neural search techniques to optimize legal content accessibility across platforms like Westlaw and CoCounsel. This role requires a solid foundation in applied science, focusing on design and deployment of production-grade search systems.

With over 5 years of industry experience, you will focus on query understanding and re-ranking strategies critical for maximizing search relevance. Your work will significantly influence the legal sector by enhancing content discovery. Key Responsibilities:

- Design production-ready neural search systems for legal applications
- Develop advanced models for document re-ranking
- Build evaluation frameworks for search performance




- Drive architecture decisions affecting index and retrieval strategies
- Collaborate closely with engineering for system scalability Requirements:
- Advanced degree (PhD or Master's) in relevant fields
- 5+ years of post-degree experience in shipping search systems
- Research publications in high-impact venues
- Strong proficiency in Python; knowledge of PyTorch
- Hands-on experience with deep learning and neural IR Utilize your skills in AI and retrieval systems to revolutionize legal information access at Thomson Reuters.

📌 Applied Scientist specializing in Information Retrieval (Toronto)
🏢 Thomson Reuters
📍 Toronto