Applied Scientist, Search & Information Retrieval

Applied Scientist, Search & Information Retrieval About the Role This is an applied science position focused on building and deploying production-grade search systems that power Westlaw, PracticalLaw, and CoCounsel. You will work across neural information retrieval, semantic and hybrid search, re-ranking, and query understanding — delivering search quality and relevance at scale for legal and professional content. About You You hold a PhD or Master's in Computer Science, AI, NLP, or a related field, with 3 years of post-degree industry experience shipping search or retrieval systems into production. You have hands-on depth in neural IR and deep learning for NLP, you work independently, and you measure success by what performs in production. What You'll Do Design, build, and deploy end-to-end neural search systems including dense retrieval, hybrid search, semantic chunking, embedding models, cross-encoders, SLM re-rankers, and transformer-based approaches Develop models for query understanding, document re-ranking, and retrieval quality optimisation Build evaluation frameworks — component-level and end-to-end — using expert annotation and synthetic data generation Drive independent technical decisions on retrieval architecture, indexing strategy, ranking models, and evaluation methodology Partner with engineering on delivery, reliability, and scale across multiple product lines Contribute to published research at venues such as SIGIR, ECIR, NeurIPS, ACL, EMNLP, and ICLR, and to intellectual property Required Qualifications PhD or Master's in Computer Science, AI, NLP, or a related field 3 years of post-degree industry experience shipping search, retrieval, or RAG systems into production — not research-only experience Publications at SIGIR, ECIR, NeurIPS, ACL, EMNLP, ICLR, or equivalent Production Python and experience with PyTorch, DeepSpeed, Torchtune, or LlamaFactory Hands-on production depth required in: Neural IR fundamentals: BM25, hybrid search, dense retrieval (DPR, ColBERT), bi-encoders, cross-encoders, late interaction models Search and RAG system design: vector databases, retrieval strategies, document chunking, metadata filtering, re-ranking, context optimisation, and orchestration Evaluation framework design for retrieval quality at component and system level Post-training of large language models and their application to retrieval systems Deep learning and NLP fundamentals Preferred Qualifications Search, QA, or RAG over large corpora and long documents, including legal or enterprise search Multi-stage or agentic retrieval architectures and query understanding for complex information needs Legal domain applications: case law retrieval, precedent finding, document review AzureML or AWS SageMaker #LI-LP2 What’s in it For You? Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected. Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance. Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future. Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition ... (truncated, view full listing at source)