About the team:
Elsevier’s mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics. As the landscape of science and healthcare evolves, we are pioneering intelligent discovery experiences — from Scopus AI and LeapSpace to ClinicalKey AI, PharmaPendium, and next-generation life sciences platforms. These products leverage retrieval-augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines.
About Role:
This role supports Elsevier’s large-scale research platforms by turning experimental NLP, search, and GenAI models into secure, reliable, and scalable production services. It focuses on ML and LLM engineering across cloud platforms, including building end-to-end ML pipelines, MLOps infrastructure, and CI/CD for models used in search, recommendations, and RAG-based systems. The position involves designing and operating retrieval, ranking, and evaluation pipelines, including IR metrics, LLM quality metrics, and A/B testing, while optimizing cost and performance at scale. You will collaborate closely with product managers, domain experts, data scientists, and operations engineers to deliver high-quality, responsible AI features over a massive scholarly corpus. The role suits an experienced ML engineer with strong cloud, search, and NLP expertise who wants to work at the intersection of GenAI, research content, and production-grade systems.
Key Responsibilities
ML & LLM Engineering, Search and Recommendation Engines
Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI)
Maintain and version model registries and artifact stores to ensure reproducibility and governance
Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment.
Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML.
End-to-end custom SageMaker pipelines for recommendation systems.
Design and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted
Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs
Build evaluation pipelines: offline IR metrics (e.g., NDCG, MAP, MRR), LLM quality metrics (e.g., faithfulness, grounding), and A/B testing.
Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization
Stay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systems
Collaboration
Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions
Collaborate and interface with Operations Engineers who deploy and run production infrastructure.
Required Qualifications
5+ years in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
Strong Python, Java, and/or Scala engineering
Experience with statistical analysis, machine learning theory and natural language processing
Hands-on‑ experience with major cloud vendor solutions (AWS, Azure and/or Google)
Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr/ Neo4j).
Experience in evaluating LLM models
Background with scholarly publishing workflows, bibliometrics, or citation graphs
A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics
Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark
Experience with large scale data processing systems, e.g., Spark
Why join us?
Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.
Work in a way that works for you
We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.
Flexible working hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
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.
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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