HelloFresh
Meal Kit Company
SeniorStaffMachineLearningEngineer,MenuPersonalisation
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“Senior Staff Machine Learning Engineer, Menu Personalisation at HelloFresh. Skills: Machine Learning, Recommender systems, ML systems, Personalization. Set technical direction for ML systems. Own end-to-end ML stack”
What You'll Achieve.
Improve customer experience; Strengthen weekly habit; Advance ML craft
Industry & Context.
Root cause analysis; Troubleshooting
What They're Looking For.
Must Have
8+ years building production ML systems, Track record of technical leadership at scale, Architectural decisions held up over years, Fluency across data and ML stack, Hands-on experience across pipelines, Hands-on experience with model serving, Hands-on experience with observability at scale, Statistical literacy to design experiments, Operational judgment to diagnose system misbehavior, Hands-on experience with AI tooling, Practical sense of AI context quality, Product sense, Bias to take full ownership
Nice to Have
Production experience with recommender systems, Production experience with large-scale personalization
What You'll Do.
Set technical direction for ML systems
Own end-to-end ML stack
Take research to production systems
Partner with Data Scientists on services
Meet latency requirements
Meet scalability requirements
Meet observability requirements
Shape personalization roadmap
Back point of view with data
Back point of view with user evidence
Operate what you build
Instrument systems in production
Improve systems in production
Raise technical bar across team
Conduct architecture reviews
Set example on production ML craft
Shape long-term architecture
Make platform decisions
Drive engineering excellence beyond personalization
Set standards for ML teams
Set standards for data teams
Sync with peers across HelloFresh
Contribute to company-wide initiatives
How You'll Work.
Team & Collaboration
Partner with Data Scientists; Partner with Data Engineers; Partner with Backend Engineers; Partner with Product; Cross-functional teams; Global collaboration; Active knowledge sharing
Communication Scope
Present point of view
Process & Methodology
Roadmap planning
Full Job Description
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 underne
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