This role is ideal for someone with hands-on experience in applied AI, NLP, information retrieval, and LLM-based applications, who enjoys building innovative solutions and translating emerging AI techniques into impactful product capabilities.
Key responsibilities
Applied AI & Research
Develop and improve LLM-powered research workflows, including:Scientific question answeringLiterature summarizationSemantic exploration and discoveryResearch insight generationCitation-aware retrieval and reasoning workflows
Build and iterate on agentic and multi-step AI workflows using frameworks such as LangGraph and related orchestration tools.
Apply modern techniques in:NLPGenerative AIEmbeddings and semantic representationsRetrieval-augmented generation (RAG)AI reasoning and workflow orchestration
Evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption.
Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts.
Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows.
Search, Retrieval & RAG Systems
Design, develop, and optimize search and retrieval pipelines, including lexical, vector, and hybrid retrieval approaches.
Contribute to the development and enhancement of RAG systems that integrate LLMs with trusted scientific and biomedical content.
Experiment with embeddings, re-ranking models, chunking strategies, and retrieval orchestration techniques to improve relevance and answer quality.
Support development of semantic search, ranking, and knowledge discovery capabilities.
Collaborate with engineering teams to deploy and scale AI-powered solutions.
AI Evaluation & Experimentation
Develop and apply evaluation frameworks for search and AI systems, including:IR metrics (e.g., NDCG, recall, precision)LLM and RAG evaluation metrics (e.g., grounding, faithfulness, hallucination detection)
Build and maintain evaluation datasets, benchmark suites, and annotation workflows.
Conduct offline experiments and contribute to online experimentation and A/B testing.
Analyze experimental results and communicate findings to stakeholders.
Contribute to responsible AI practices focused on quality, reliability, and trust.
Cross-functional Collaboration
Partner with product managers, engineers, UX researchers, and domain experts to deliver AI-powered capabilities.
Communicate technical findings and recommendations clearly to both technical and non-technical audiences.
Contribute to knowledge sharing and adoption of best practices across the Platform Data Science organization.
Support delivery of projects from research and experimentation through production deployment.
Required qualifications
Master’s or PhD in Computer Science, Data Science, Machine Learning, NLP, Information Retrieval, or a related field
Experience in data science, machine learning, applied NLP, information retrieval, generative AI, or a related field
Hands-on experience with:LLM-based applications and generative AI systemsRAG pipelines and retrieval systemsSearch and retrieval architectures (lexical, vector, hybrid)Evaluation methodologies for IR and generative AI systems
Strong programming skills in Python
Experience with modern AI/ML frameworks and tooling (e.g., PyTorch, Hugging Face, LangChain, LangGraph, Haystack)
Experience working with Databricks or similar distributed data and machine learning platforms
Understanding of experimentation methodologies, evaluation frameworks, and statistical analysis
Proficiency with data visualization and analytical tooling (e.g., Tableau, Power BI, matplotlib, seaborn)
Demonstrated ability to independently execute technical projects and contribute to cross-functional initiatives
Preferred qualifications
Experience building AI assistants, agentic workflows, or conversational AI applications
Experience working on search, ranking, recommendation, or retrieval systems
Familiarity with scientific, biomedical, or scholarly datasets
Experience with knowledge graphs, ontologies, or semantic enrichment systems
Exposure to production ML systems and MLOps practices
Academic or industry research experience in NLP, information retrieval, search, or generative AI
Experience working in content-rich, knowledge-intensive, or highly regulated domains
Working for you
We know that your well-being and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:
Comprehensive Pension Plan
Home, office, or commuting allowance.
Generous vacation entitlement and option for sabbatical leave
Maternity, Paternity, Adoption and Family Care leave
Flexible working hours
Personal Choice budget
Internal communities and networks
Various employee discounts
Recruitment introduction reward
Employee Assistance Program (global)
About the business
As a global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education, and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world’s grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.
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- We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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