Lead Applied Scientist, Search & Information Retrieval
Core
Design, develop, and production deploy large-scale search and information retrieval systems for legal, tax, and accounting content.
Role type
Lead Applied Scientist (Search & Information Retrieval)
Builds
Search architectures, indexing pipelines, ranking/re-ranking systems, and self-service search platforms for Thomson Reuters products.
Domain
Legal technology, Information Retrieval, Machine Learning, NLP
Deliverable
production ML models
Required skills
Search engine architecture, indexing systems, ranking and re-ranking, semantic/hybrid retrieval, query understanding, relevance optimization, LLM-enhanced retrieval (RAG), distributed model development, production Python, PyTorch, Hugging Face Transformers
Preferred skills
Legal/tax domain experience, enterprise knowledge repositories, Elasticsearch/OpenSearch/Solr/Vespa, API platform development, Agentic AI systems, AzureML/AWS SageMaker
Responsibilities
Design search architectures for large-scale content; Build ingestion pipelines; Develop ranking systems using traditional and LLM approaches; Improve retrieval quality via semantic/hybrid methods; Design evaluation frameworks; Lead technical decisions on indexing and infrastructure; Partner with engineering teams; Mentor applied scientists.
Seniority
Senior, hands-on IC with mentorship responsibilities
