Top Machine Learning Jobs in the Netherlands

Design, build and scale an automated translation-quality evaluation framework for localisation and dubbing. Implement production ML evaluation infrastructure, roll it out across teams, align metrics with product signals, and maintain/iterate systems in collaboration with R&D and cross-functional partners.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

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

ScreenPoint Medical seeks talented individuals to join their teams, focusing on AI applications for breast cancer. Candidates can submit CVs and motivation letters for consideration.

As a UX Writer at Nebius, you'll improve user experience by creating UI copy, collaborating with teams, and developing guidelines.

Design, develop, and productionize GenAI solutions (RAG, agentic AI). Drive roadmap priorities, integrate capabilities into business and data platforms, collaborate with stakeholders, ensure scalability/reliability, and mentor junior engineers.

Lead a team of researchers and engineers in developing and deploying advanced AI-driven recommender systems for eBay's eCommerce platform, leveraging ML and NLP techniques.

The Applied AI Engineer will onboard clients on Mistral AI products, manage complex integrations, and drive technological transformations through collaboration with stakeholders.

As an AI Research Intern, you will tackle AI research questions, build prototypes using AI tools, and design evaluations for AI systems while collaborating with fellow engineers and product managers.

The Senior AI Engineer will design and develop advanced ML and NLP solutions for healthcare, collaborating across teams and mentoring juniors.

Seeking an AI/ML Engineer to develop and optimize machine learning models for mobile apps, collaborating with teams to enhance user experiences.

End-to-end delivery of ML-powered services, involving research, prototyping, production, and operations. Collaborate on product requirements while maintaining high quality and operational excellence.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

Lead ML infrastructure upgrades in hyperscale data centres across EMEA: manage contractors, schedules, logistics, mechanical/electrical/fibre coordination, commissioning (L3/L4 support), risk mitigation, reporting, and lessons learned throughout full project lifecycle.

Build ML prototypes and data pipelines, collaborate with researchers and product engineering to integrate models into Fusion, containerize and deploy applications, implement best practices, and document architectures to accelerate research-to-production workflows.

Optimize ML model training pipelines for speed and reliability, applying GPU optimization techniques and mentoring team members on performance best practices.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

Lead end-to-end ML systems for LLM/NLP document intelligence and supply-chain predictive analytics. Design and fine-tune transformer-based models, implement RAG pipelines, build forecasting, classification and optimization algorithms, and productionize models using containerization and MLOps tools. Ensure robust, explainable models deployed to secure on-premise environments with monitoring and continuous improvement.

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.

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.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

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.

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 and prototype ML solutions to improve developer workflows (code completion, generation, agents, test generation). Apply or train open-source models, build reproducible training and evaluation pipelines, stay current with ML-for-code research, collaborate with colleagues, and provide mentorship.