Top Machine Learning Jobs in the Netherlands

Leads analytical methodology and evaluation for AI-based healthcare and pharmaceutical workflows. Owns model selection, cohort and HCP segmentation, agent decision logic, and evaluation frameworks. Partners with engineering, data architecture, and governance teams to productionize rigorous analytical methods, identify new data and techniques, communicate insights, and mentor data science and engineering colleagues.

Leads data science methodology and advanced analytics for AI-enabled Medical Affairs workflows. Develops and evaluates statistical, machine-learning, NLP, and AI models using healthcare and real-world data; defines validation, monitoring, and decision frameworks; partners with engineering, governance, and business teams to operationalize solutions. Provides technical leadership, mentorship, documentation standards, and strategic guidance while ensuring privacy, compliance, responsible AI use, and clear communication of analytical findings.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Lead the AI and innovation direction for Operations, identify and prioritize high-value use cases, and build a central Ops innovation model. Manage a small Forward Deployed AI team, partner with domains and engineering to deliver scalable solutions, track delivered value, and drive adoption and upskilling across the function.

Establish and scale FareHarbor’s revenue management and dynamic pricing practice. Analyze booking patterns, seasonality, channels, market conditions, and customer performance to improve revenue and yield. Develop onboarding standards, playbooks, training, tools, escalation paths, and implementation processes. Advise strategic customers while partnering with Product, Commercial, Data, Machine Learning, Implementation, and Customer Success teams to translate insights into product opportunities and measurable outcomes.

Lead the development of Adyen Protect, focusing on machine learning models for fraud detection, infrastructure scaling, and team mentorship.

Lead and develop globally distributed Data and AI teams to deliver production Data/AI solutions. Drive talent development, operational excellence, stakeholder alignment, and responsible AI practices while partnering with Product and Engineering to set priorities, roadmaps, and delivery metrics.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Design, build, deploy, and continuously improve data analyses, dashboards, and AI-driven solutions for Compliance. Translate compliance challenges into robust, explainable, and maintainable analytical products. Own solutions end to end, including responsible AI assessments, documentation, monitoring, refactoring, and technical debt reduction. Collaborate with Compliance, engineering, data, and analytics teams to deliver production-ready solutions and contribute to organization-wide AI best practices.

Design, build, deploy, and monitor production-ready ML solutions (batch and real-time). Create data pipelines, automate experiments and training, collaborate with MLOps, run experiments/A-B tests, analyze large datasets, and translate business requirements into ML solutions (fraud, AML, optimization, anomaly detection, representation learning).

Design, implement, train, deploy, and monitor production machine learning algorithms for optimization, fraud detection, risk, identity, AML, and behavioral analysis. Build data and training pipelines, automate experimentation and monitoring, collaborate with MLOps, analyze complex datasets, define performance metrics, conduct A/B tests, and communicate findings to product and business stakeholders.

Build and operate production machine learning systems at scale, including data ingestion, model training, validation, deployment, monitoring, and optimization. Integrate ML solutions with products, transition research prototypes into scalable services, and collaborate with software engineering, data science, MLOps, and platform teams. The role focuses on risk, fraud, AML, anomaly detection, behavioral modeling, and other financial technology applications.

Build and scale production-ready machine learning models for regulatory reporting and risk evaluation. Own the full ML lifecycle, including feature engineering, infrastructure, deployment, and data pipelines. Develop automated root-cause analysis using statistical signals and causal inference, collaborate closely with product users, and drive technical roadmaps while communicating complex outcomes to diverse stakeholders.

Designs, develops, deploys, and maintains AI/ML components, pipelines, and production-grade LLM or AI-agent systems. Responsibilities include model evaluation, data quality improvement, ETL/ELT pipelines, MLOps automation, REST backend development, secure lifecycle engineering, monitoring, observability, and guardrails. The role supports mission-critical NATO systems in secure or air-gapped environments and requires documentation, risk reporting, and collaboration on technology roadmaps.

Drive business development and close high-value enterprise deals with global biotech and biopharma accounts across Europe. Own strategic customer relationships, deliver recurring revenue, partner with technical and customer success teams on complex proposals, provide market insights for product direction, build industry credibility, and improve enterprise sales processes and playbooks.

Conduct advanced research in artificial intelligence and machine learning, including deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation. Define complex business problems, design and deliver modeling solutions, evaluate model performance, establish scientific and engineering specifications, and build analytical roadmaps. Assess production implementation requirements, improve data capture methods, apply statistical and mathematical techniques, and resolve root causes and process bottlenecks.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Consults with banks, insurers, and asset managers to build and validate statistical, predictive, and generative AI models for financial risk, AML, fraud, sanctions, and regulatory compliance. Analyzes complex financial datasets, supports AI model governance, applies machine learning to transaction monitoring, and communicates findings directly to clients. The role also involves project ownership, regulatory research, internal knowledge sharing, and identifying improvements to clients’ financial technology practices.

Own the intelligence layer for operational platforms across maritime, logistics, industry, and critical infrastructure. Build ontologies, taxonomies, knowledge graphs, domain models, decision logic, optimization solvers, and applied machine-learning systems. Establish uncertainty, explainability, measurement baselines, and model governance while ensuring production-grade architecture. Collaborate closely with customers, explain models clearly, and deliver reliable recommendations for live operational decisions.

Develop and operate production machine learning models and end-to-end MLOps pipelines for airline ancillary pricing. Responsibilities include model training, retraining, deployment, monitoring, architecture optimization, low-latency serving, infrastructure automation with Terraform, containerization with Docker, and CI/CD using GitHub Actions. The role uses GCP, BigQuery, and Vertex AI while leading MLOps practices and collaborating with data science and engineering teams.

Lead architecture and technical strategy for merchant fraud prevention across multiple teams. Design high-throughput, low-latency distributed systems, integrate ML/LLM models, evolve data and inference pipelines, align product and compliance goals, standardize practices, and mentor senior engineers.

Conduct frontier research in AI alignment science, focusing on post-training and alignment of foundation models and agentic systems. Develop a research agenda, publish at leading venues, collaborate with engineering and business teams to apply research, shape AI roadmaps, communicate findings, mentor interns and colleagues, and guide adoption of new methods across Netflix.

Leads engineering teams building scalable advertising and marketing technology platforms. Owns architecture, technical strategy, platform modernization, distributed systems, cloud-native microservices, reliability, observability, performance, and code quality. Partners with product and architecture leaders on large-scale transformations while developing high-performing teams across geographies. The role requires hands-on technical leadership, including architecture reviews, code review, and experience with AI- and ML-enabled capabilities.

Own the full enterprise sales cycle across the Benelux region, from territory development and partner engagement through solution demonstrations and deal closure. Present Darktrace’s cybersecurity solutions to CISOs and information security leaders, attend industry events, and collaborate with SDRs and technical experts. The role requires Dutch and English fluency, enterprise B2B closing experience, strong communication skills, entrepreneurial territory-building ability, and interest in cybersecurity within a high-growth environment.

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.