Top Artificial Intelligence Jobs in the Netherlands

The role involves evaluating AI outputs, generating content, and ensuring cultural relevance. Strong analytical skills and local insights are essential to ensure quality in AI training projects.

Seeking a Senior Full-Stack Engineer to develop AI-driven web and mobile applications, focusing on scalable architectures and API integrations. Collaborate with product teams to translate features into technical solutions.

Develop and deploy machine learning and AI models for seed quality, classification, and prediction. Work with diverse datasets, build end-to-end pipelines, integrate solutions into operations, collaborate with engineers and domain experts, and iterate in an Agile environment.

Work on enhancing code repositories, validating models, building web applications, and optimizing prompt complexity for AI tasks in a continuous project environment.

The role involves enhancing code repositories, validating models, developing web applications, and optimizing AI prompts. It emphasizes improving AI programming skills through practical tasks using development expertise.

Lead the AI Enablement and Execution role, focusing on building efficient workflows and systems, hiring a team, and driving organizational leverage through AI integration.

The Principal Engineer will lead the design and delivery of core platform capabilities, focusing on scalable systems, AI application, and mentoring engineers while driving architectural decisions and improving operational systems.

This role involves defining AI safety measures, governance patterns, and privacy guidance. You'll enhance the AI platform, ensuring safe and efficient product deployments.

The Senior AI Engineer will design and enhance shared AI platform capabilities while ensuring reliability and production readiness. The role requires collaboration on system architecture, building reusable components, and mentoring other engineers in AI development practices.

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.