Senior Staff Machine Learning Engineer, Menu Personalisation

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 broader ML and data craft forward.

What you'll bring

• 8+ years building and operating production ML systems, with a track record of technical leadership at scale.
• Architectural decisions in your past that held up over multiple years and influenced teams beyond your own.
• Production experience with recommender systems or large-scale personalization is a strong plus.
• Fluency across our data and ML stack (Python, Spark) and our backend and platform stack (Go, Kafka, Kubernetes), with hands-on experience across pipelines, model serving, and observability at scale.
• Statistical literacy to design honest experiments and the judgment to know when a model is actually better versus when the metrics are lying to you.
• Operational judgment to diagnose system misbehavior, find root causes, and ship systems you can debug under real load.
• Hands-on experience with AI tooling (Claude Code, Cursor, Copilot) beyond casual experimentation; you use AI agents every day and have a practical sense of how the context you provide shapes output quality.
• Product sense: opinionated about what should be built and why, with the ability to back it with evidence and translate it into business value.
• A bias to ship; you take full ownership and finish the last twenty percent.