Top Machine Learning Jobs in the Netherlands

Lead the strategy and execution of Adyen’s AML detection product. Build scalable machine learning models, rules, and risk signals; develop self-service detection using ML, GenAI, and agentic approaches; establish effectiveness metrics; and partner with Compliance, Legal, Risk, Operations, data scientists, analysts, and engineers. The role requires prioritization, regulatory judgment, stakeholder influence, and leadership of a cross-functional product team in a highly regulated fintech environment.

Build and operate production machine learning pipelines for Adyen’s anti-money laundering systems. Modernize rules-based detection into an AI/ML engine, develop models using large-scale transaction data, address class imbalance and incomplete labels, and collaborate with data engineering, analytics, operations, and compliance teams. Lead projects from ideation through deployment while promoting strong software engineering, data engineering, and MLOps practices.

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.

Leads regional digital technology service delivery across EMENA and GBI, overseeing infrastructure operations, budgets, vendors, manufacturing-plant integrations, service performance, and technology strategy. The role drives AI-first and cloud-first adoption, operational improvements, responsible AI practices, organizational change, and stakeholder engagement across multiple countries. It also manages regional teams, supports new plant launches, ensures Industry 4.0 alignment, and delivers cost, compliance, and customer satisfaction targets.

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.

Develop machine learning models, analyze complex datasets, identify actionable insights, and deliver scalable data solutions for clients. The role involves statistical modeling, predictive analytics, data visualization, experimentation with AI and data science techniques, and collaboration with diverse teams. Candidates will use Python, R, SQL, Tableau or Power BI, communicate fluently in Dutch and English, and help organizations make data-driven decisions.

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.

Own the strategy and execution of integrated B2B marketing campaigns for enterprise AI solutions and a self-service platform. Drive pipeline, product adoption, acquisition, and category awareness through ABM, content, events, paid media, lifecycle, social, partnerships, PR, and sales activation. Coordinate internal teams and agencies, optimize messaging and conversion paths, and measure performance across account engagement, pipeline, product adoption, and awareness.

Leads CAE’s enterprise Safety and Quality Intelligence capability by standardizing analytics, data governance, KPIs, taxonomies, dashboards, and reporting. Partners with governance, operations, manufacturing, and business-unit leaders to develop an intelligence platform incorporating predictive analytics, automation, and AI. Establishes executive risk and performance insights, influences global stakeholders, and leads a team of analytics professionals.

Own the technical sales process from initial discovery through post-sales, designing Cloudera-based solutions, demonstrating products, presenting roadmaps, and partnering with account managers to secure technical wins. The role requires customer-facing technical expertise, business requirements gathering, presentations, cloud and data platform knowledge, and customer advocacy. Responsibilities also include supporting renewals, identifying use cases, collaborating with Product and Engineering, and participating in industry evangelism.

Analyze large-scale gaming datasets using statistics, machine learning, forecasting, recommender systems, generative AI, experimentation, causal inference, and econometrics. Partner with product and engineering teams to develop data-driven strategies, build business solutions, deliver actionable insights, and share technical knowledge across teams.

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.

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.

Design and deliver internal systems, Go services, APIs, data models, dashboards, visualizations, and LLM-powered workflow features. The role includes writing specifications, estimating work, code reviews, testing, documentation, security, performance, accessibility, and collaboration with nontechnical stakeholders. The engineer will handle sensitive people data, mentor colleagues, and improve team practices in a globally distributed environment.

Own the anti-fraud product roadmap across signup, payments, account protection, compute-abuse prevention, risk scoring, manual review, customer verification, and appeals. Partner with Trust & Safety, Engineering, Data Science, Analytics, and Legal to define detection rules, models, incident responses, experiments, and compliance processes. Monitor fraud losses, chargebacks, false positives, review throughput, and detection speed while minimizing customer friction.

Lead scientific delivery for partner materials-discovery projects, from scoping through results. Apply simulation, machine learning, data-driven optimization, or experiments; design discovery campaigns; validate models against experiments; and translate between experimental, computational, and ML teams. Build reliable automated workflows, improve data pipelines, prepare technical reports, and relay partner feedback to technical teams. The role requires deep expertise in materials science, chemistry, physics, chemical engineering, or related fields, along with Python and Git proficiency.

Design, develop, deploy, automate, and support secure, scalable software applications and infrastructure across Red Hat Linux and AWS environments. Gather requirements, integrate enterprise systems and databases, troubleshoot issues, participate in architecture and Agile/DevOps activities, and drive continuous improvement. The role also involves documentation, stakeholder engagement, testing, quality assurance, mentoring junior engineers, and supporting semiconductor design infrastructure and HPC environments.

Develop and improve transaction-monitoring detection rules and analytical models to identify money laundering, terrorist financing, and other financial crime. Analyze large customer datasets, identify relevant data sources, write reliable production code, assess model challenges, and collaborate with business experts, model owners, IT specialists, and analysts. The role focuses on reducing false-positive alerts and enabling financial-crime analysts to prioritize genuine risks.

Own and improve production machine learning pipelines focused on accuracy, efficiency, and trustworthiness. Build LLM evaluations, judges, datasets, metrics, and benchmarks; run experiments across prompts, models, and pipeline designs; and reduce LLM costs through smaller task-specific models. Expand anonymization capabilities, evaluate open-source models, and collaborate with quality engineers to ship reliable ML features. Document findings and communicate technical trade-offs clearly.

Conduct quantitative research for systematic equity strategies, including alpha-signal development, portfolio construction, alternative-data analysis, and machine-learning applications. Translate academic research into investable ideas, test models, present findings internally, and improve research tooling and processes while collaborating with global investment teams.

Design, implement, deploy, and monitor production machine learning systems for payment optimization, identity verification, fraud prevention, AML, and behavioral analysis. Build large-scale data and training pipelines, automate experimentation and model monitoring, analyze complex datasets, conduct statistical experiments, and define performance metrics. Collaborate with product managers and stakeholders while providing hands-on technical leadership across the machine learning lifecycle.

Build and operate production machine learning systems at scale, including data ingestion, training, validation, deployment, and monitoring pipelines. Optimize model training and inference performance, integrate ML solutions with products, and transition research prototypes into scalable services. Collaborate with software engineering, data science, MLOps, and platform teams across payment optimization, identity verification, disputes, AML, fraud prevention, and anomaly detection use cases.

Build and improve Firefox WebCompat internal dashboards, reporting tools, and broken-site screenshot capture pipelines. The role involves creating reliable data views for triage and metrics, automating screenshot workflows, collaborating with WebCompat and QA teams, and translating reporting needs into effective tooling. Familiarity with WebCompat workflows, Bugzilla, screenshot automation, and machine learning is preferred.

Develops and automates electrical characterization, tuning, and performance measurement protocols for quantum devices. Builds instrument-control, data-acquisition, analysis, and processing pipelines using Python and applies AI or machine learning where appropriate. Works hands-on in cryogenic laboratory environments, improves measurement throughput and reproducibility, communicates technical insights across disciplines, and maintains code quality, documentation, data integrity, experiment traceability, and security compliance.

Develops data analytics, data science, and AI solutions for internal audit, including pipelines, automations, APIs, machine-learning analyses, and reusable tools. Translates audit and risk questions into reliable, evidence-based insights; strengthens data governance and code quality; and trains auditors through coaching and bootcamps. The role also drives the internal audit data and AI roadmap, supports technology innovation, and collaborates internationally across Ayvens and Societe Generale.