Senior Machine Learning Engineer

Build scalable recommendation systems that help millions of customers discover the products they need, when they need them.

How do you make our customers happy?

With 13 million customers, 129 million visits per month and about 41 million products on display, bol is the most successful online retail platform in the Netherlands and Belgium. Team Recommendations helps customers navigate bol’s scale by making product discovery more personal, relevant and timely. That means combining data, machine learning and engineering craft to make life easier and more fun for customers, while learning fast and sharing what works along the way.

The biggest challenge

The biggest challenge is helping customers discover the right products and content at the right moment in their journey. That can mean supporting a purchase decision, completing an order, or inspiring a customer to explore more of the assortment. With millions of products, thousands of partners and changing customer intent, this is not a trivial problem. You will help detect where customers are in their journey, generate recommendations that are relevant and commercially viable, and serve them in real time with models that are fast, reliable and scalable.

What you will do as Senior Machine Learning Engineer

Team Recommendations creates personalised and non-personalised recommendations for customers across bol. The team works across the full data science life cycle: understanding current solutions and hurdles, experimenting with better approaches, aligning with stakeholders, and bringing the most promising solutions into production.

As a Senior Machine Learning Engineer, you turn complex machine learning ideas into reliable products by choosing suitable architectures, frameworks and technologies, and by solving the technical, data-related and computational challenges that come with operating ML at scale. You communicate your solutions to technical, business and operations audiences, and you work with your team and stakeholders to make practical use of current leading technologies.

• Scale out analyses and algorithms to improve recommendations
• Collaborate on improving current ML pipelines so recommenders stay up to date and easy to maintain
• Work together with the team to bring data science solutions for recommendations into production

Why you can make a difference

You can make a difference in this role if you enjoy turning complex data and machine learning challenges into practical solutions that work in the real world. We are looking for someone who loves working with data, brings curiosity and creativity to the team, and is comfortable collaborating closely with others to move from ideas to impact. You do not need every answer upfront; what matters is that you can make thoughtful trade-offs, learn quickly, and help the team make progress while keeping quality in mind.

In Team Recommendations, priorities can shift as the team learns more about customers, systems and the opportunities ahead. That means you should feel at home in an iterative way of working, where the end-state is not always fully known from the start. You balance short-term delivery with long-term technical viability, take ownership of the systems and solutions you work on, and proactively look for ways to improve reliability, scalability, efficiency and costs. If you combine a “getting things done” mindset with engineering care and a healthy sense of humour, you will fit right in.

Technically, you bring solid engineering experience and hands-on machine learning expertise. Ideally, you have at least 5 years of engineering experience, including at least 2 years as a Machine Learning Engineer, and you are comfortable building production-grade solutions in Python. You know your way around cloud platforms, Kubernetes, SQL and modern data or ML pipelines, and you understand what it takes to keep machine learning systems maintainable after they go live.

A strong fit for this role has practical experience with the tools used every day to operate recommendations at scale: Airflow for orchestrating data and ML workflows, dbt for transforming and modelling data in a maintainable way, and Dataflow for scalable data processing. These tools are essential to how recommendation pipelines are built, run and improved, so we are looking for someone who can use them confidently in a production environment.

Familiarity with GitLab CI, Vertex AI, recommender systems at scale, Kotlin or Java is a plus. More important than ticking every possible box is your ability to apply these skills thoughtfully, make pragmatic technical choices and grow together with the team.

3 reasons why this is (not) for you

What will help you succeed

• Enjoy working at the intersection of AI, software engineering and data engineering, where ideas only count once they run reliably in production
• Like building on public cloud services, or are eager to learn how to use them to create scalable, maintainable ML products
• Enjoy explaining technical choices clearly and taking your team and stakeholders along as ML solutions move from experiment to production

What may make this role a poor fit

• You do not lose sleep over messy code, unclear ownership or hard-to-maintain pipelines, as long as the model seems to work for now
• You never give much thought to the costs associated with working at scale
• You only want to train models and are not interested in the engineering work needed to bring them into production and keep them reliable

Where you'll be working

As a Machine Learning Engineer, you’ll be at the centre of our data-intensive landscape. Our business model is focused on technology and data-driven improvements that drive innovation for our customers. The data science community at bol is an ever-growing team of Data Scientists, Machine Learning Engineers, Data Engineers and Software Engineers, alongside a specific MLE community. The cross-functional teams, with representation from business units, give you a complete picture of the organization. Professional success in data science requires more than any university can teach you, and bol is the perfect place to learn the rest. The setting is informal, pragmatic and innovative, with success based on collaborating as equals and continuously learning from each other.

Perks of having a blue heart

• 29 days to recharge
• Travel costs: Public transport, car, parking & charging covered
• Pension plan: 75% premium covered
• Annual bonus: Based on sustainability goals