Role overview
You’re curious, hands-on, and excited to turn ideas into working solutions. You enjoy working across teams and care about building things that actually make a difference.
- 6+ years of experience as a Machine Learning Engineer or Data Scientist
- Strong foundation in machine learning theory and practical application
- Comfortable working with Python and object-oriented design principles
- Experience with at least two of the following: Hydra, Dagster, AWS, Github Actions, Docker, Kubernetes
- Solid understanding of data preprocessing and feature engineering
- Experience integrating third party LLM’s in a production environment
- Experience in designing and creating ML-ops pipelines
- Strong communication skills and ability to work cross-functionally
- A proactive mindset and eagerness to learn and improve
- You’re based in The Netherlands, are an EU resident, or have an existing visa to work here (a relocation package isn’t available)
What you'll work on
- Build and apply machine learning solutions to improve support operations, metadata enrichment, and ticket processing
- Design and develop ML models that solve real product and operational challenges
- Own the full model lifecycle from prototyping to deployment and continuous improvement
- Improve and scale our MLops infrastructure to support experimentation, deployment, and monitoring
- Work on systematizing the use of LLM’s in the company, setting up evaluation and monitoring guidelines and protocols for various applications
- Partner closely with Product, Data, Operations and Engineering to understand problems and deliver solutions
- Turn data into actionable insights that drive product and business decisions
- Contribute to the adoption of LLMs and AI-driven features across the platform
- Continuously improve how we use data and machine learning to create impact for fans and organisers
What we're looking for
- Experience with ETL processes and data pipelines
- Experience with Redshift, DBT or Clickhouse
- Background in statistics or statistical modelling
Tags & focus areas
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