Machine Learning Engineer, Consumer Alliance

HelloFresh's HelloTech organisation is the global technology backbone with more than 1000 people, building digital products powering the end-to-end food experience across meal kits, ready-to-eat meals, pet food, and premium meat & seafood. It creates platforms serving millions of customers monthly through a subscription-based direct-to-consumer model, supporting customer-facing apps, personalization, pricing, forecasting, and supply chain optimization. The company operates in autonomous, cross-functional alliances responsible for products or domains. The Menu Personalization team handles recommender systems matching customers to recipes across global markets, including Data Scientists, Backend Engineers, Data Engineers, ML Engineers, and Product roles.

• 3+ years of experience building and operating production ML systems.
• Strong production experience with recommender systems or large-scale personalization is a plus.
• Fluency with the data and ML stack including Python, Apache Spark, and working knowledge of backend and platform stack technologies such as Kafka and Kubernetes.
• Statistical literacy for designing experiments and evaluating model metrics.
• Operational judgment to diagnose and fix system issues under load.
• Hands-on experience with AI tooling such as Claude Code, Cursor, Copilot.
• Analytical product sense, translating technical initiatives into business value.
• Strong bias to ship with accountability for project completion.

• Build and operate ML systems for menu personalization, covering feature pipelines, training workflows, model serving, experimentation tooling, and infrastructure.
• Transition research and experiments into reliable production systems with Data Scientists.
• Maintain accountability for significant parts of the recommender stack from design to operation.
• Instrument and improve systems in production to ensure continuous refinement based on performance.
• Contribute to the personalization roadmap with Product and Engineering, using data and user evidence.
• Raise the technical bar via code and design reviews, technical guidance, setting an example for production ML craft.

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