Top Machine Learning Jobs in the Netherlands

Lead the Production Inference team of research scientists and ML engineers. Own people development, technical direction, research and development roadmaps, architecture decisions, and model-serving reliability, efficiency, latency, and cost performance. Partner with research, infrastructure, product, and commercial stakeholders; guide production deployment and lifecycle improvements; and recruit technical talent. The role emphasizes leadership and technical reviews rather than hands-on coding.

Leads the Product function, portfolio strategy, product prioritization, development, launch, and market adoption for clinical AI and medical imaging products. Builds and coaches the Product Management organization, aligns engineering, AI, clinical, regulatory, commercial, and partner teams, and guides roadmap decisions using clinical evidence and customer insights. Requires extensive software product leadership, regulated SaMD and medical imaging experience, cloud or SaaS delivery expertise, and the ability to operate across complex stakeholder environments.

Conduct quantitative research on financial markets by discovering predictive patterns, developing statistical and machine learning models, running large-scale simulations and backtests, and translating research into production-ready trading signals. Interns collaborate with researchers and technologists, build research infrastructure and AI-powered agents, and complete rigorous projects from hypothesis through evaluation under realistic market conditions.

Conduct quantitative research on market behavior and trading strategies. Responsibilities include discovering predictive patterns, designing statistical and machine-learning models, building AI research agents, developing large-scale research infrastructure, running experiments and backtests, and translating findings into production-ready trading signals. Interns complete an end-to-end research project while collaborating with researchers and technologists.

Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.

Lead and develop Customer Success Managers across Southern Europe, especially France and Spain. Ensure enterprise customers successfully launch and derive measurable value from the product, build executive and technical stakeholder relationships, translate product capabilities and performance data into business outcomes, establish scalable customer success processes, and handle complex escalations.

Develop quantitative models for sports pricing, odds compilation, and risk management. Analyze large datasets, identify trading insights, deploy models with engineers, monitor performance, conduct back-testing, and continuously improve model accuracy. Collaborate with data scientists and engineers to align models with business needs. The role requires Python, sports betting market knowledge, statistical modeling, and experience with odds and margin optimization.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Lead Adyen’s global in-person payments product strategy, overseeing hardware, software, payments, loyalty, and AI/ML initiatives. Build and scale a world-class product organization, align engineering and cross-functional partners, define measurable roadmaps, and engage strategic enterprise customers. The role requires executive-level product leadership, deep technical fluency, hardware-software experience, payments expertise, and experience delivering AI-powered products. It is based in Amsterdam and is office-first.

Build scalable software, data pipelines, and AI solutions for vegetable R&D analytics. Integrate scientific data, models, and workflows into intuitive applications; deploy and monitor production systems in cloud environments; collaborate with scientists and analytics teams; and evaluate agentic AI and automation responsibly. Ensure solutions are explainable, reliable, scientifically valid, and compliant with governance and security standards.

Lead product marketing for the French market by developing localized positioning, messaging, sales enablement, launch campaigns, and customer insights. Partner cross-functionally with product, sales, marketing, engineering, design, communications, and partnerships teams. Track adoption and market performance, identify growth opportunities, and lead product marketing strategy for a major Paris customer event. The role requires French and English fluency, B2B technology marketing experience, and travel within France and internationally.

Build and maintain TypeScript-based analytics and advertising tools across React front ends, Node.js services, ETL pipelines, and data layers. Optimize ClickHouse and MongoDB workloads, maintain testing and CI quality, stabilize background processing and real-time delivery, and own architecture and features end to end. Mentor engineers and solve ambiguous technical problems through production delivery.

Own the end-to-end strategy, roadmap, and outcomes for a network intelligence and security product serving ISP customers. Partner with engineering, data science, security, and compliance teams on architecture, ML evaluation, privacy, and production delivery. Conduct customer discovery, analyze market and threat trends, define requirements and metrics, build business cases, and represent the product to technical customers, executives, sales, marketing, and public audiences.

Own the strategy, roadmap, and measurable outcomes for Catawiki’s search, ranking, recommendations, and personalization products. Lead cross-functional initiatives from discovery through experimentation and scale, using customer research, behavioral data, AI, machine learning, and A/B testing to improve relevance, engagement, and buyer conversion. Partner with engineering, design, data science, marketing, and commercial teams while communicating strategy and results to senior stakeholders.

Thesis internship in Forecasting Analytics: analyze demand-forecast data, test statistical and machine-learning forecasting methods, support projects, identify accuracy improvements, and present findings to improve forecast processes for part of the Mars Wrigley portfolio.

Supports AOCS and pointing engineering for ESA space programs, including requirements definition, system trade-offs, performance analysis, industrial oversight, verification and validation, procurement, FDIR, mission operations, R&D, simulation, and facility testing. The role also contributes to spacecraft integration, in-flight activities, technology development, and advanced guidance, navigation, control, and optimization techniques.

Supports the design, development, integration, validation, deployment, and monitoring of AI and data science solutions for ESA space programs. Responsibilities include data collection and preprocessing, exploratory analysis, predictive modeling, machine learning, image processing, anomaly detection, data visualization, and use of HPC-based information factory environments. The role also contributes to AI standards, technology development plans, knowledge sharing, and digital innovation initiatives.

Leads JET’s enterprise-wide data and AI governance strategy, including data quality, metadata, lineage, stewardship, data products, responsible AI, regulatory compliance, and governance tooling. Partners with Technology, Product, Legal, InfoSec, and business leaders across global markets to establish scalable standards, drive adoption, shape data strategy, and develop a high-performing governance team.

Own client AI engagements from roadmap and architecture through production and long-term performance. Establish data platform, MLOps, and LLMOps standards; build production-grade AI and agentic systems; mentor engineers; contribute to hiring, account leadership, and engineering practices; and translate technical complexity into decisions for nontechnical stakeholders.

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.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Design and implement cloud-based data solutions and scalable data pipelines for financial services clients. Migrate legacy Excel models to Azure, support AI/ML and LLM integrations, develop data architectures, and collaborate with consultants and actuaries to productionize analytical applications while applying software engineering best practices.

Build production-grade machine learning and AI solutions for client projects and internal products. Lead an internal AI product team, make cloud architecture decisions, scale data science models into production, and mentor engineers. The role also includes training, client workshops, knowledge sharing, conference presentations, and thought leadership. Candidates need strong Python skills, additional programming experience, cloud and DevOps expertise, big data experience, and fluency in English and Dutch.

Leads client data science consulting projects involving machine learning, statistics, optimization, simulation, forecasting, econometrics, and big data. Manages projects, client relationships, proposals, publications, and internal offerings while supervising and developing junior consultants. Requires strong quantitative expertise, Python proficiency, additional programming experience, and professional Dutch and English.

Collaborate with data scientists, data engineers, and consultants on client projects. Apply statistical modeling, optimization, machine learning, Generative AI, and large language models to solve business problems. Extract actionable insights, present analytical findings, and support client workshops. The role requires strong programming, technical communication, and fluency in English and Dutch.