Machine Learning Engineer - Network Analytics

This is what we offer you

• Gross monthly salary between EUR 4,292 and EUR 6,131 (scale 09) for a 36 hour week
• Thirteenth month's salary and 8% holiday allowance
• 11% Employee Benefit Budget
• EUR 1,400 development budget per year
• Hybrid working: balance between home and office work (possible for most roles)
• A pension, for which you can set the maximum amount of your personal contribution

View all our benefits.

As a Machine Learning Engineer in our Graph Analytics Team, you’ll play a critical role in tackling complex challenges like entity matching & resolution in graph data. Whether it’s detecting duplicate entities across large, interconnected datasets or identifying hidden relationships within networks, your work will enable more accurate insights and better decision-making. You’ll leverage machine learning and graph analytics to design scalable entity matching solutions for real-world problems, ensuring seamless integration into production systems via Knowledge Graphs. Collaborating with a multidisciplinary team, you’ll have the opportunity to explore innovative approaches to solving entity resolution challenges while delivering impactful results.

Practical examples

• Work directly with internal customers to understand use cases involving graph data, such as fraud detection, entity resolution, or network analysis.
• Develop, train, and optimize graph-based machine learning models.
• Build reusable graph processing and ML pipelines that can be shared across teams.
• Pair-program with engineers to integrate ML models into scalable, production systems.

Facts & figures

• 36 or 40 hours a week.
• Mostly greenfield, limited legacy work.
• 43,822 Rabobank colleagues around the world.

Top responsibilities

• Build, train, test, deploy, and maintain graph machine learning models and systems.
• Collaborate with data engineers to ensure seamless ETL pipelines and scalable processing of graph data.
• Keep up-to-date with the latest developments in graph machine learning and data analytics to improve team practices.
• Give and receive help from peers, experts, and stakeholders so we can improve together.

To be considered for this position, you must be located in the Netherlands or elsewhere in the EU and have the legal right to work in the Netherlands. We are unable to consider candidates requiring relocation from another country.

Together we achieve more than alone

We believe that bringing together people's differences is what makes us an even better bank. Talking of Rabobank: We are a Dutch bank that operates in 38 countries for over 9,500,000 customers. Together with these customers, our members, and partners we stand side by side to create a world in which everyone has access to enough healthy food. In the Netherlands we work to create a country in which people are happy with how they live, work, and do business.

Within Rabobank, Enterprise Analytic Products is an extremely ambitious department that aims to mix in a healthy dose of experience, innovation, and creativity to develop innovative products and services. We centrally create re-usable analytical building blocks, to make an impact across the bank. We focus on fast learning, delivering value quickly and being more effective. As a team, you work on clear goals and continuously see the results of your (team) efforts.

You and your talent

• At least 2 years of experience developing and deploying machine learning models in production environments.
• Strong understanding of machine learning algorithms, with a focus on supervised and unsupervised methods for graph data.
• Experience with cloud platforms (preferably Azure) and containerization tools (Docker, Kubernetes).
• You have experience in unit testing, integration testing, and end-to-end testing, with a focus on writing clean, maintainable, and testable code that adheres to industry best practices.
• Competence in building CI/CD pipelines for automated model deployment and testing.
• You have some demonstrable experience with Python, GitHub, and Azure Cloud implementations.
• You are curious by heart and have experience with collaborating with customers.
• You have particularly effective communication skills in the English language (both written and spoken).
• A passion for learning and applying new technologies.
• A big plus is to have hands-on experience with graph analytics and graph machine learning frameworks (e.g. Neo4j) and having some experience with building Gen AI applications (prompting, OpenAI API, agents, etc.).

You and the job application process

• Any questions about working at Rabobank and the process? Raphaël Drenthel, IT Recruiter via raphael.drenthel@rabobank.nl. 
• We will hold the interviews through a video call.
• You can find answers to the most frequently asked questions on rabobank.jobs/nl/veelgestelde-vragen.
• A security check is part of the process.
• We respect your privacy.