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Senior Staff Machine Learning Engineer, Menu Personalisation (m,f,x)

hellofresh · Berlin, Berlinsenior

In short

  • ▸Ingeniero ML de alto impacto que dirige sistemas de recomendación a escala global.
  • ▸Trabaja en todo el ciclo: de experimentos a producción, con foco en latencia, escalabilidad y datos reales.
  • ▸Influencia estratégica: define arquitecturas que afectan a toda la compañía durante años.

Proficiency in English required for collaboration across global teams.

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What they ask for

  • ✓Experiencia comprobada en sistemas de recomendación a escala.
  • ✓Capacidad de llevar modelos de ML de prueba a producción confiable.
  • ✓Conocimiento profundo de pipelines de features, entrenamiento y servido de modelos.
  • ✓Experiencia en arquitectura de sistemas de baja latencia y alta disponibilidad.
  • ✓Habilidades de liderazgo técnico con influencia sobre equipo y plataformas.
  • ✓Capacidad para tomar decisiones basadas en datos y pruebas con usuarios.

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About HelloFresh At HelloFresh, we want to change the way people eat forever by offering our customers high-quality food and recipes for different meal occasions. Even after celebrating our 10-year anniversary, we continue to see this mission spread around the world and beyond our wildest dreams. Now, we are a global food solutions group and the world's leading meal kit company, active in 18 countries across 3 continents. So, how did we do it? Our weekly boxes full of exciting recipes and fresh ingredients have blossomed into a community of customers looking for delicious, healthy and sustainable options. The HelloFresh Group now includes our core brand, HelloFresh, as well as: Green Chef, EveryPlate, Chefs Plate, Factor_, YouFoodz, The Pets Table and GoodChop. About the Team Menu Personalization decides what millions of customers see when they open HelloFresh each week. The team owns the recommender systems that match customers to recipes across our global markets, and brings together Data Scientists, Backend Engineers, Data Engineers, ML Engineers, and Product to take ideas from experiment to production. The work directly shapes customer experience and business growth: when personalization gets better, customers find recipes they love faster, and HelloFresh becomes a stronger weekly habit. At HelloFresh we are moving away from a model where software developers just execute tickets toward one where engineers are trusted to own customer problems. You take a problem, form a point of view, validate it with customers and data, and ship it using AI as a force multiplier. About the Role We are looking for a technical leader for the Menu Personalization ML systems, someone who owns the recommender stack that runs in production. You will set the direction for how we design, build, and operate the ML systems behind menu personalization, while staying hands-on across feature pipelines, training workflows, model serving, experimentation tooling, and the infrastructure underneath. The decisions you make here shape the platform for years, not quarters, and you will be expected to hold a point of view on where personalization at HelloFresh should go and to back it with data and user evidence. You will partner with Data Scientists to take models from notebook to production, with Data Engineers on features and pipelines, with Backend Engineers on online inference paths, and with Product on what to build next. Your influence will reach well beyond Menu Personalization: through the standards you set, the architectural decisions you make, the engineers you grow around you, and the company-wide initiatives you drive to advance ML and data engineering craft across HelloFresh. What you'll do • Set the technical direction for the ML systems behind menu personalization, owning the end-to-end stack: feature pipelines, training workflows, model serving, experimentation tooling, and infrastructure. • Take research and experiments to reliable production systems, partnering with Data Scientists on services that meet real latency, scalability, and observability requirements. • Shape the personalization roadmap with Product and Engineering leadership, backing your point of view with data and user evidence. • Operate what you build, instrumenting and improving your systems in production because shipping is the beginning of the learning cycle, not the end of it. • Raise the technical bar across the team through architecture reviews, mentorship, and the example you set on production ML craft. • Shape long-term architecture and make platform decisions whose payoff plays out over years rather than quarters. • Drive engineering excellence beyond Menu Personalization by setting standards other ML and data teams adopt. • Sync with peers across HelloFresh on best practices and contribute to company-wide engineering initiatives that move the

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