Top Machine Learning Jobs in the Netherlands

Supports ESA software product assurance activities by defining requirements, reviewing contractor plans and compliance, monitoring software processes and documentation, participating in technical and review boards, conducting assessments and analyses, and maintaining databases for nonconformances, requests for waiver, and action items. The role requires software engineering expertise, space engineering knowledge, ECSS experience, and familiarity with software quality, security, safety, configuration management, and assurance practices.

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.

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.

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 and optimize machine learning models for computer vision and NLP applications, including reverse image search, image-text matching, object detection, and online content analysis. Train deep learning models, build scalable image and text data pipelines, monitor production performance, and implement advanced algorithms. Collaborate with data scientists, engineers, and brand protection experts to improve AI product accuracy and performance.

Develop, validate, deploy, and maintain machine learning solutions for medical device manufacturing and CAD/CAM systems. Build deep learning and multimodal AI models using computer vision, 3D geometry, meshes, images, and structured data. Integrate models into production systems, conduct performance analysis, maintain ML workflows, and translate research into practical manufacturing applications while collaborating with engineering, manufacturing, and product teams.

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.

Build and deploy real-time voice and video AI models, including speech-to-speech, multimodal, and audiovisual systems. Adapt and fine-tune pretrained models, develop evaluation and data pipelines, optimize GPU inference for latency and cost, and deliver production features across backend APIs, databases, and customer portals. Own projects end to end while using coding agents and development harnesses to improve implementation, testing, debugging, and review.

Own platform-level optimization for production LLM and VLM inference. Responsibilities include improving latency, throughput, memory efficiency, GPU utilization, reliability, quality, and cost; deploying and extending inference engines; building compression and acceleration workflows; developing benchmark and rollout frameworks; diagnosing performance bottlenecks; and providing technical direction across machine learning, GPU, infrastructure, research, product, and customer teams.

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.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

As a Staff ML Engineer, lead the development of machine learning systems for advertising, drive strategic data decisions, optimize system performance, and mentor engineers.

Leads the strategic direction of independent medical education and scientific content solutions. Provides clinical oversight, develops evidence-based content strategies and therapeutic plans, ensures medical accuracy and defensibility, and partners with editorial, product, project, commercial, and scientific stakeholders. The role monitors clinical evidence and regulatory developments, advises on complex content decisions, engages key opinion leaders and customers, and translates complex medical and scientific information into accessible educational products.

Leads the adoption of AI across Systems Engineering and RAMS practices. Identifies and scales AI use cases for requirements, architecture, modeling, verification, validation, traceability, FMEA, data analysis, and simulation. Develops methodologies, standards, and reusable frameworks; leads pilots and proofs of concept; coaches engineering teams; and collaborates with software, data science, quality, and innovation stakeholders to deliver practical, scalable AI-enabled engineering solutions.

Leads the software engineering, applied ML, and technical product teams responsible for Protolabs’ enterprise pricing engine and supporting services. Owns the pricing capability roadmap, architecture, rules-based and model-driven decisioning systems, and production LLM capabilities. Oversees solution design, development, testing, governance, reliability, and continuous improvement while partnering with pricing analysts and service-line leaders. Hires, mentors, and develops a cross-functional technical team.