Senior AI Search & Retrieval Architect

Thomson Reuters Corp.; New York (NY); On-site; USD 148,000 - 274,000; Full time

Job summary

Thomson Reuters Corp. is seeking an experienced Lead Applied Scientist to design, deploy, and optimize large-scale search systems powering Westlaw, Practical Law, and CoCounsel. You will own architectures for indexing, retrieval, and ranking, and mentor teams across the organization.

You will contribute to semantic and hybrid retrieval, RAG approaches, and end-to-end evaluation frameworks, delivering measurable production impact and scalable search services.

Qualifications

• PhD in Computer Science, Information Retrieval, AI, ML, NLP, or a related field preferred
• 8+ years of industry experience building production search, information retrieval, ranking, or recommendation systems
• Publications at SIGIR, ACL, EMNLP, NeurIPS, ICLR, KDD, WWW, or equivalent venues
• Strong production Python skills and experience with PyTorch, Hugging Face Transformers, and distributed model development

Responsibilities

• Design and deploy large-scale search architectures for legal, tax, and enterprise content
• Build ingestion pipelines to analyze, enrich, and prepare documents for retrieval
• Develop ranking and re-ranking systems using traditional IR techniques and LLM-based approaches
• Improve retrieval quality through semantic and hybrid retrieval
• Lead technical decisions around indexing, architectures, and search infrastructure

Skills

Production Python

PyTorch

Transformers

Distributed ML

Search systems

Mentoring

Evaluation metrics

Education

PhD in Computer Science or related field

Tools

Hugging Face Transformers

Elasticsearch

OpenSearch

Solr

Vespa

AzureML

AWS SageMaker

Job description

Thomson Reuters Corp. is seeking an experienced Lead Applied Scientist to design, deploy, and optimize large-scale search systems powering Westlaw, Practical Law, and CoCounsel. You will own architectures for indexing, retrieval, and ranking, and mentor teams across the organization.

You will contribute to semantic and hybrid retrieval, RAG approaches, and end-to-end evaluation frameworks, delivering measurable production impact and scalable search services.