Top Machine Learning Jobs in the Netherlands

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Design, build, and maintain MLOps tools, automation, and end-to-end ML pipelines. Support large-scale GPU clusters, implement monitoring/observability, streamline reproducible training and deployment, and collaborate with product and engineering teams to optimize ML workflows for scalability and cost-efficiency.

Lead and manage a team of ML engineers and researchers to design, deploy, and monitor scalable computer vision, graphics, and generative AI models for a VFX platform. Define ML strategy, collaborate with product and engineering to integrate models into production pipelines, ensure responsible AI practices, and partner with infrastructure teams to build efficient training and deployment systems.

Own and scale half-hourly forecasting models for frontline workforce demand, lead end-to-end client launches (data validation, model selection, UAT, monitoring), join client calls, build stakeholder relationships, and maintain production ML systems that handle messy data and feedback loops.

Develop and deploy ML/AI models for financial prediction and anomaly detection; collaborate with teams on strategy and data analysis.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

Design, build, and deploy AI-powered features for a digital asset management platform using AWS AI services and LLM APIs. Implement embedding-based search, agentic AI workflows, prototypes, and collaborate with product and engineering teams to transition prototypes into production.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

The intern will optimize biological AI models for efficiency, focusing on profiling bottlenecks, implementing compression techniques, and benchmarking inference pipelines.

Design and deploy intelligent systems, productionize models, collaborate with stakeholders, and develop tools for cloud-based Machine Learning platform.

As a Lead ML Engineer, you'll design and build recommendation systems, combining AI and user preferences to enhance shopping experience, overseeing the ML lifecycle from models to deployment.

Train and evaluate machine learning models, build predictive models, design feature pipelines, and integrate models into production systems.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.