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.

Responsibilities

• 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

Requirements

• 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.
• Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
• Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.

Benefits

Additional Information

Applied Scientist, Search & Information Retrieval

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