Applied Scientist, NLP​/IR​/GenAI

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** Join us in building Thomson Reuters' next-generation search that powers everything from direct user queries to cutting-edge GenAI applications across our product suite. We're conducting advanced research to apply modern, state-of-the-art techniques to deliver the most accurate and comprehensive search for legal and professional information.

About the role

As an
** Applied Scientist**, you will:
* ** Innovate & Deliver**:
Design, build, test, and deploy end-to-end AI search solutions using neural information retrieval techniques, semantic and hybrid search, and re-ranking approaches. Develop models for information retrieval, semantic search, document re-ranking, and query understanding, including dense retrieval architectures, semantic chunking models, embedding models, cross-encoders, SLM re-rankers, and transformer-based LLM-driven approaches. Work in collaboration with engineering to ensure well-managed software delivery and reliability at scale.
* ** Evaluate & Optimize**:
Develop comprehensive data and evaluation strategies for both component-level and end-to-end quality, leveraging expert human annotation and synthetic data generation. Apply robust training and evaluation methodologies to optimize retrieval quality and latency.
* ** Drive Technical Decisions**:
Independently determine appropriate retrieval architectures, indexing strategies, ranking models, data, and evaluation strategies for IR and NLP problems. Solve search relevance, ranking, and scalability challenges in a self-directed manner while contributing effectively as part of a multidisciplinary team.
* ** Align & Communicate**:
Partner closely with Engineering and Product to translate complex challenges into scalable, production-ready solutions. Engage stakeholders to deeply understand business problems and domains, shaping objectives and goals that align AI search capabilities with product needs and business objectives.
* ** Advance the Field**:
Publish at top venues (e.g., SIGIR, ECIR, NeurIPS, ACL, EMNLP, ICLR) and contribute to patents to keep our solutions cutting-edge and competitive.
** About You
*** PhD in Computer Science, AI, or a related field, or a Master's with equivalent research/industry experience.
* 3+ years of hands-on experience building and deploying modern search or RAG systems with neural retrieval methods and deep learning models for NLP.
* Strong background in information retrieval fundamentals, including indexing, query processing, ranking and relevance modelling.
* Strong programming skills (e.g., Python) and experience with modern deep learning frameworks (e.g., PyTorch, Deep Speed, Torchtune, Llama Factory).
* Proven ability to translate complex problems into innovative AI applications.
* Publications at relevant venues such as SIGIR, ECIR, NeurIPS, ACL, EMNLP, ICLR.Technical Qualifications
* Deep understanding of neural information retrieval fundamentals: BM25, hybrid search, dense retrieval (e.g., DPR, ColBERT), cross-encoders, bi-encoders, late interaction models
* Hands-on experience designing and implementing search or RAG systems: vector databases, retrieval strategies, document chunking, metadata filtering, hybrid search, re-ranking, context optimization, and orchestration
* Experience developing relevant datasets and evaluation frameworks
* Solid understanding of ML and deep learning approaches for NLP
* Solid understanding and experience with post-training of large language models and their application to retrieval systems

Preferred Qualifications
* Extensive prior work on search, question answering or RAG over large corpora and long documents, including experience with legal or enterprise search systems
* Experience with multi-stage or agentic retrieval architectures and query understanding for complex information needs.
* Experience building applications for the legal domain (e.g., legal search, case law retrieval, precedent finding, document review, document drafting).
* Publications at relevant venues such as SIGIR, ECIR, NeurIPS, ACL, EMNLP, ICLR.#LI-LP2
* ** Hybrid Work Model:
** We’ve adopted a flexible hybrid working environment (2-3 days a week in the…