Top Machine Learning Jobs in the Netherlands

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

Lead product strategy and execution to grow buyer discovery, engagement, conversion and retention. Prioritise opportunities using data and research, run experiments and A/B tests, partner with engineering, design and data science, and scale AI-enabled recommendation and personalization features to deliver measurable customer and business outcomes.

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

Lead and deliver petrophysics, seismic petrophysics and rock physics consulting projects and training. Develop best-practice workflows, support sales with technical proposals, publish and present technical work, and build customer relationships to grow consulting services and software adoption.

As an AI Product Manager, you will define the product roadmap, collaborate with teams to develop AI features, conduct market research, and ensure ethical AI use.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.

Own and evolve a cloud-agnostic data and ML platform supporting ingestion, storage, processing, model training, deployment, and lifecycle management. Build reliable, scalable, automated infrastructure using DevOps and MLOps practices, including CI/CD, Infrastructure-as-Code, containerization, monitoring, governance, security, and cost optimization. Collaborate with data science, ML engineering, and IT teams while maintaining vendor-delivered components, documentation, and operational processes.

Work on client-facing analytics projects: design solutions, build prototypes, implement ML/NLP models (e.g., PD models, decarbonization, churn, GenAI chatbots), validate results, and communicate findings to clients and colleagues across industries.

Design, build, and deploy scalable AI/ML and Generative AI solutions for enterprise systems. Develop Python-based production applications, APIs, microservices, and MLOps/CI-CD pipelines. Implement data ingestion, preprocessing, feature engineering, model training/validation, deployment, monitoring, and lifecycle management. Collaborate with DevOps, data engineering, and product teams to ensure secure, compliant, and high-performance AI solutions.

As a Data Analyst, you will develop data strategies, enhance data governance, implement data quality processes, and provide data-related insights to clients.

Design, prototype, and deploy ML and LLM solutions using messy, real-world data. Build data pipelines, models, visualizations and PoCs. Communicate insights to technical and non-technical stakeholders and iterate quickly to deliver business impact.

The position involves conducting research and teaching in mathematics-enhanced AI for biomedical applications, mentoring students, and advancing interdisciplinary initiatives.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

As a Machine Learning Research Intern, you will conduct research on HFT strategies, analyze market opportunities, and collaborate on strategy implementation using ML techniques.

Join a trading team to analyze global markets and develop profitable trading strategies using machine learning and statistical models.

The Senior Quantitative Researcher will lead the development and deployment of ML-based statistical arbitrage strategies while mentoring team members and collaborating with internal teams.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.