Top Machine Learning Jobs in the Netherlands

As AI Strategy Lead, you'll manage key clients and develop AI strategies for tech scale-ups, leveraging your consulting experience and network.

As a Data Analyst, you'll advise clients on data management strategies, implement solutions, and enhance data governance and quality. You'll also provide insights for business decisions based on analytical data evaluations.

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.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

Design and lead enterprise-scale data and AI architectures for global clients. Develop prototypes and proof-of-value demonstrations, govern AI agents, integrate cloud platforms and external systems, support pre-sales, establish technical standards, and mentor teams. The role requires translating business needs into secure, scalable technical solutions while driving cloud data platform adoption, data governance, modeling, and AI-driven transformation.

Lead the strategic and technical direction of LLM and agentic AI initiatives while managing and developing a high-performing team of AI engineers and researchers. Design, develop, and deploy advanced AI solutions, mentor team members, oversee performance and capacity planning, and align AI roadmaps with business objectives. Stay current with generative AI, NLP, and autonomous systems, communicate with technical and non-technical stakeholders, and champion Agile delivery practices.

Own and build Polaroid’s People Operations function, including HRIS architecture, people data, employee lifecycle processes, compliance, payroll inputs, benefits, and mobility support. Lead cross-functional projects and modernize employee services through AI automation, workflows, chatbots, and policy knowledge bases. Manage HR technology integrations, data quality, dashboards, compensation cycles, and employment-law processes across jurisdictions while partnering with Finance, IT, Legal, Payroll, and Talent Acquisition.

Research and investigate global fraud trends using BioCatch data and external sources. Produce data-driven insights, visualizations, written thought leadership, dashboards, and presentations. Build repositories and automate analysis workflows while applying fraud expertise and AI tools to validate findings and support sales and marketing.

Lead the design, development, deployment, and continuous improvement of Generative AI and machine learning capabilities. Build systems involving LLMs, multimodal models, agents, retrieval, embeddings, model adaptation, and feedback-based learning. Make architectural decisions across the ML lifecycle, optimize quality, latency, cost, and reliability, and partner with research, engineering, product, and platform teams. Mentor engineers and provide hands-on technical leadership from experimentation through production.

Own the AI product vision, roadmap, prioritization, and delivery from ideation through launch. Translate business needs into technical requirements while collaborating with engineers, data scientists, marketers, and commercial teams. Drive AI adoption, monitor trends, define success KPIs, and ensure responsible, ethical, and privacy-compliant product usage.

Develop Elastic observability and security solutions for application and infrastructure teams. Build Kibana user interfaces and REST APIs, analyze IT operations data, create Elastic rules and SIEM detections, implement Elastic Security modules, and automate zero-touch deployments with Ansible. Collaborate with customers, UI teams, product management, and IT Operations while improving infrastructure automation and service quality. The role is hybrid in the Netherlands and requires Dutch nationality, a Dutch passport, and Dutch-language proficiency.

Develop Elastic-based security and observability solutions for applications and infrastructure. Build Kibana user interfaces and REST APIs, analyze IT operations data, create Elastic and SIEM detection rules, implement Elastic Security modules, and automate zero-touch deployments with Ansible. Collaborate with customers, UI teams, product management, and IT Operations to improve monitoring, security, automation, and operational efficiency.

Lead redesign of B2B underwriting models and credit frameworks, improve data analysis and processes, support multi-country product expansion, partner with Data Science on ML models, enhance reporting and KPIs, and collaborate with cross-functional teams to steer B2B risk strategy.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

Lead and develop a team of Data Scientists and ML Engineers building production machine-learning systems for logistics. Drive AI adoption, roadmap prioritization, agile execution, and measurable business outcomes. Partner with Product Managers, Staff ML Engineers, and the MLOps platform team to deploy scalable solutions. The role requires deep hands-on experience with production ML, recommender systems, pricing algorithms, statistical models, and modern data and AI technologies, alongside strong people leadership and executive communication skills.

Lead Workday Compensation implementations and HCM transformations: run workshops, gather requirements, configure, test, deploy, lead workstreams, liaise with clients and teams, and apply Accenture/Workday methodologies and AI-enabled tools.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Ten-week, hands-on quantitative research internship applying mathematics, statistics and programming to market data to develop and test systematic trading strategies. Includes structured training, collaborative research projects, simulated trading, exposure to traders/researchers, and AI applications. Strong performers may receive full-time offers.

Join as a graduate quantitative researcher to design, build, test and deploy statistical and machine learning models for pricing, forecasting and signal discovery using large-scale market and unstructured data. Collaborate with researchers, traders and engineers to monitor production models, refine assumptions, and accelerate research-to-production using AI-enabled workflows.

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.

Build and productionize multi-agent systems and agentic workflows for personalized e-commerce recommendations. Design agent coordination, communication, planning, evaluation, tool use, safety controls, and orchestration techniques. Address latency, cost, and reliability challenges at eBay scale while collaborating with researchers, engineers, product, design, and analytics teams. Use large multimodal datasets and A/B testing to improve marketplace outcomes.

Build and own cloud data infrastructure and ETL pipelines on major cloud platforms. Collect, transform, and serve large-scale data; create reusable integrations and orchestration for multi-use cases while working in Agile teams.

Design, build, and maintain large-scale data environments and reusable ETL pipelines. Collect and transform data from multiple sources, develop integration services in cloud or hybrid infrastructures, and apply orchestration, API development, and ML/AI knowledge to ensure high-performance, production-ready datasets.

The AI Lead is responsible for managing AI teams, driving AI project strategies, and ensuring deployment efficiency in telecommunications. They will oversee AI R&D, mentor team members, and collaborate across teams to align AI solutions with business goals.

Conduct original research in AI and machine learning, develop deep learning architectures, analyze large datasets, and explore new methodologies.