Top Machine Learning Jobs in the Netherlands

Provide management with performance insights, translate MI needs into practical Power BI and SQL dashboards, manage data definitions and reports, support data scientists with data models, and act as the link between data and business insights.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

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 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.

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.

Lead Vegetation Intelligence product, owning roadmap from ML detection models to customer-facing risk products. Advance detection models, integrate outputs into outage and ignition risk, run user research, and translate model capability into actionable B2B products used in the field.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Build and operate ML platform systems for training, evaluation, deployment, and serving. Improve reliability, scalability, cost-efficiency, observability, and automation for GPU and cloud workloads. Develop internal tools and agent-friendly workflows, collaborate with researchers and product engineers, and drive platform architecture and developer experience.

As a Staff ML Engineer, lead the development of machine learning systems for advertising, drive strategic data decisions, optimize system performance, and mentor engineers.

The Product Analyst will optimize EMS strategies through data analysis, collaborate with stakeholders, implement solutions, and develop predictive models.

Dragonfly is seeking Software Engineers for various portfolio companies, focusing on crypto technologies. Engineers of all experience levels are needed across multiple stacks.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

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

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

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

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

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Design, build, deploy, and operate production-grade ML systems and end-to-end ML pipelines. Focus on MLOps and platform development, model APIs, CI/CD automation, monitoring, and scalable infrastructure using cloud, containers, orchestration, and IaC.

Design, build, and optimize scalable data pipelines and ML/AI frameworks. Develop, train, deploy, and tune ML models (including NLP, computer vision, recommender systems) at enterprise scale. Apply GenAI tools, ensure data governance, security, and performance, support AI infrastructure and platform deployments, and contribute to CoE standards and reusable frameworks.

Lead design, build, deployment, and operation of production-grade ML systems. Create end-to-end ML pipelines, CI/CD for models, manage cloud-native infrastructure, implement monitoring/model-drift detection, and collaborate with data scientists and product managers to productionise ML models.

Develop and implement 3D geometric ML and classical geometry algorithms to analyze and generate 3D intraoral scans and teeth. Build data pipelines, train/adapt 3D models, optimize test-time performance, validate robustness, and collaborate with clinicians and product teams to ship applied research into products.

Design, build, and maintain production-grade ML systems for revenue management. Own end-to-end ML lifecycle: data pipelines, model development, deployment, monitoring, and testing. Collaborate with product and engineering, define SLIs/SLOs, mentor engineers, and drive technical strategy to deliver scalable, reliable pricing and revenue-optimization features.