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Y-Axis in the Netherlands is seeking a Senior Machine Learning Engineer to turn complex ML ideas into reliable products. You’ll choose architectures, frameworks and technologies, solving data and compute challenges to operate ML at scale.
You will communicate results to technical, business and operations audiences and work with your team and stakeholders to apply leading technologies in practice, driving impact across recommendations.
Team Recommendations creates personalised and non-personalised recommendations for customers across bol. We work 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.
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 we learn more about our customers, our systems and the opportunities in front of us. 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.