Machine Learning Engineer developing next - generation algorithms for Zoox’s autonomous vehicles. Collaborating on complex driving scenarios to ensure safe navigation in urban environments.
About the role
- Senior MLOps Engineer supporting Elsevier’s research platforms with GenAI expertise. Focusing on ML pipeline automation, model deployment, and collaborative data science solutions.
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
- 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.
Requirements
- 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
Benefits
- 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)
Job title
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