Senior Machine Learning Engineer for Search

As a Machine Learning Engineer, you'll play a key role in designing, building, and deploying machine learning solutions that power bol.com's data-driven innovations. Working in cross-functional teams alongside data scientists, software engineers, and business stakeholders, you'll transform tested models into scalable, real-time production systems that directly enhance customer experiences.

• 5+ years' experience as an ML Engineer, ideally in Search or Recommendations
• Strong Python, SQL, and data engineering skills (Airflow, DBT, Kubernetes, CI/CD)
• Experience deploying ML in production on cloud platforms (GCP preferred)
• Comfortable with agile workflows, A/B testing, and MLOps best practices

Pros

• You like working on the overlap between AI, software and data engineering
• You have an A/B test mindset and like real-time visible solutions in a high impact area of the company
• You like to communicate and take your team and stakeholders along in moving solutions to production

Cons

• You do not believe in an agile/iterative approach and only want to build models from data without taking them into production
• You are not flexible and can't shift smoothly between different topics and produce spaghetti code
• You are not comfortable if the design of the end-state is not known yet and do not like coding, scripting, templating

As a Machine Learning Engineer, you'll play a key role in designing, building, and deploying machine learning solutions that power bol.com's data-driven innovations. With 13 million customers, 129 million monthly visits and about 41 million products on display, bol is the most successful online retail platform in The Netherlands and Belgium. And when we say 'platform', we mean it. Well over 49,000 external partners - and counting - enrich the product base. Data-driven, adventurous, we intend to deliver on our promise to make life easier and more fun for customers. And that is exactly what our IT 750+ professionals (25% internationals) do. Making mistakes is no biggie, as long as learnings are shared.