6+ years experience in search and retrieval systems
Deep expertise in lexical, vector, and hybrid retrieval
This role leads a team of applied scientists advancing lexical, vector, and hybrid retrieval systems to power Elsevier's next-generation AI ecosystem
Job Summary
This role leads a team of applied scientists advancing lexical, vector, and hybrid retrieval systems to power Elsevier's next-generation AI ecosystem.
The successful candidate will define robust evaluation frameworks combining traditional IR metrics with GenAI-specific measures like factual consistency and hallucination rates.
Elsevier offers flexible working hours, comprehensive pension plans, sabbatical leave, and a culture focused on innovation and responsible AI standards.
Matching Summary
This role leads a team of applied scientists advancing lexical, vector, and hybrid retrieval systems to power Elsevier's next-generation AI ecosystem.
Skills & Requirements
Must-have
PhD or MSc in Computer Science or Data Science
6+ years experience in search and retrieval systems
Deep expertise in lexical, vector, and hybrid retrieval
Proficiency in Python, PyTorch, Hugging Face, LangGraph, or Haystack
Experience building scalable evaluation frameworks
Nice-to-have
Experience deploying retrieval-enhanced LLMs in production
Familiarity with scientific ontologies like MeSH, UMLS, ORCID
Strong stakeholder management and communication skills
Prior experience in academic publishing or research intelligence
Knowledge of knowledge graphs and semantic enrichment