Top Machine Learning Jobs in the Netherlands

Lead strategic, full-cycle recruitment for specialized technology and product management roles across EMEA and global tech hubs. Partner with hiring leaders on workforce planning, candidate profiles, sourcing strategies, and inclusive, data-driven hiring. Build passive talent pipelines, deliver high-touch candidate experiences, support onboarding, share market intelligence, and improve recruiting practices and employer brand.

As a Senior AI Research Engineer, you will drive Adyen's AI strategy, build production-grade applications, optimize AI interactions in finance, mentor teams, and engage with the AI community.

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.

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.

Leads a multinational BDR organization of approximately 18 representatives through team leaders. Owns BDR strategy, playbook development, performance management, metrics, sales technology adoption, process optimization, and cross-functional alignment with Sales, Marketing, and senior leadership. The role combines strategic planning with hands-on operational leadership across European and US markets, driving scalable prospecting, qualification, conversion, and team development.

Own enterprise sales for Algolia’s AI-powered search and discovery platform across Global 2000 organizations. Manage complex, multi-stakeholder sales cycles, develop territory and account strategies, expand existing accounts, generate new opportunities, and partner with Solutions Engineering on demos, proofs of value, and ROI analyses. Forecast using MEDDPICC, articulate AI and agentic capabilities to technical and executive stakeholders, and collaborate with SDRs, Customer Success, Legal, Marketing, and partners.

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.

Develops market impact, volume, volatility, and execution models for an equity trading desk. Uses statistics, machine learning, reinforcement learning, stochastic methods, and optimization to improve pre-trade and real-time execution decisions. Analyzes large intraday datasets, builds data infrastructure, evaluates broker algorithms, and communicates findings through tools, dashboards, and reports. Requires strong Python and quantitative research skills, financial-market affinity, Azure familiarity, and the ability to explain complex concepts clearly.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.

Develop and automate qubit experiments, calibration routines, and quantum measurement systems. Improve measurement throughput and reproducibility using automation, AI, and machine learning. Collaborate across quantum research, hardware, and software teams; communicate technical findings; and build production-quality data and analysis pipelines. The role requires expertise in low-noise electronics, cryogenic systems, RF reflectometry, qubit control, and experimental quantum physics.

Safeguard client assets and oversee Dutch and European fund regulatory compliance. Responsibilities include reviewing fund transactions, investment restrictions, asset valuations, NAV calculations, cash flows, and income distributions; conducting due diligence and risk assessments; documenting reviews; resolving errors and breaches with fund managers; onboarding new funds; and preparing client and regulatory reports.

Designs and maintains scalable data models, ETL/ELT pipelines, data quality controls, dashboards, and analytics solutions using SQL, Python, BigQuery, and dbt. The role supports data-driven business decisions through analysis, forecasting, documentation, access management, user training, and troubleshooting. It also contributes to AI agent development, automation, business planning, and regional forecasting while collaborating with data scientists, engineers, analysts, and business stakeholders.

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

Six-month quantitative research internship for master's students, involving quantitative model development, data analysis, programming, large datasets, result interpretation, and presentation. Interns work on projects spanning factor investing, machine learning, data engineering, NLP, trading and liquidity, or sustainability, supervised by experienced researchers. Applicants may complete a company-supervised thesis or conduct a non-thesis applied research project.

Leads the design, development, deployment, and evaluation of machine learning, forecasting, recommendation, and analytical solutions. Partners with Product, Commercial, Engineering, and Data teams to solve business problems, define metrics, build scalable pipelines, and deliver measurable impact. Provides technical leadership and mentorship, promotes model development and governance best practices, and contributes to the data science roadmap.

Develop predictive and prescriptive models, prepare and analyze datasets, engineer features, communicate insights through visualizations and dashboards, and deploy and monitor machine learning models. The role partners with data engineers, data translators, and business stakeholders to deliver AI and advanced analytics solutions that create measurable business value.

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.

Owns full-cycle recruiting for commercial, operational, and technical roles across global markets. Responsibilities include sourcing passive candidates, building pipelines for hard-to-fill positions, advising hiring leaders, coordinating interviews and offers, maintaining accurate Greenhouse data, and improving hiring processes through systems thinking, data, AI, and automation.

Lead data science consulting projects, translate business problems into analytics solutions, manage client relationships, oversee project delivery, identify growth opportunities, and guide junior team members. The role involves working across the data lifecycle, applying statistical and machine learning expertise, presenting insights to business stakeholders, and coordinating multidisciplinary teams.

Lead data science consulting engagements, manage client relationships, translate business problems into analytics solutions, oversee project delivery, and guide analytical teams. Advise senior business leaders, communicate complex concepts clearly, identify growth opportunities, and mentor junior team members. The role requires strong statistical, programming, database, visualization, and machine learning expertise, along with substantial consulting or industry experience and project leadership.

Leads data science projects for client organizations, translating business problems into impactful analytics solutions. Manages client relationships, provides project oversight, identifies new opportunities, presents analytical findings, and guides junior team members. The role involves the full data lifecycle, including acquisition, cleaning, integration, analysis, interpretation, visualization, and machine learning, while coordinating multidisciplinary teams and communicating complex concepts to business stakeholders.

Leads technical security assessments, cyber risk audits, vulnerability investigations, and architectural reviews across global businesses. Reports findings to CTOs and CISOs, translates technical risks into business recommendations, coordinates high-stakes security initiatives, and supports technical experts. The role also builds the global cybersecurity community, organizes events, participates in hackathons, and investigates emerging AI, ML, LLM, and agent security risks. Travel of up to 30% is required.

Develop and productionize machine learning algorithms that extract insights from satellite imagery time series for agricultural, land management, and climate applications. Own marker quality and delivery across EU countries, collaborate with scientists and engineers, explore embeddings and AI-agent workflows, maintain shared code, document research, and communicate findings through technical and client-facing reports.

Leads and grows a six-person Data Science and Analytics team supporting customer-service agent tooling. Defines support-quality metrics, guides experimentation and machine-learning practices, accelerates model deployment, and shapes team strategy and roadmaps. Partners with Product, Engineering, Design, Support Operations, and Vendor Management to improve help resolution. Responsible for hiring, mentoring, organizational design, technical standards, tactical planning, and cross-functional decision-making.

Lead end-to-end delivery of complex enterprise software engagements from kickoff through go-live, establish PMO standards and reporting, manage risks and cross-functional dependencies, support scope and contract reviews, and build customer success onboarding and learning systems. The role also shapes a new global PMO function and partners with Customer Success, Product, and Engineering to improve delivery excellence and operational scalability.