Staff Backend Engineer, Customer Value Optimisation

The Role

We're looking for a Staff Backend Engineer to join our Customer Value Optimization (CVO) tribe at HelloFresh. This is a high-impact individual contributor role sitting at the intersection of Data Science and Backend Engineering — you'll be the technical bridge that turns ML models into reliable, scalable production systems.

You'll work across squads within the CVO tribe, partnering closely with Data Scientists and Backend Engineers to define how we serve ML models at scale, raise the technical bar, and accelerate the tribe's ability to deliver value to our customers.

What you’ll do

• Design, build, and own robust, scalable infrastructure for live serving of ML models in production


• Act as the technical bridge between Data Science and Backend Engineering teams within the CVO tribe — translating DS needs into scalable engineering solutions


• Define and drive best practices for model serving, APIs, and system design across squads


• Lead cross-squad technical initiatives, from architecture decisions to hands-on implementation


• Partner with Data Scientists to ensure models are production-ready — performant, observable, and maintainable


• Contribute to tribe-wide engineering standards and shape the technical roadmap


• Mentor and support engineers across squads without direct line management responsibility

What you’ll bring

• 7+ years of backend engineering experience, with a strong foundation in Python


• Proven experience building and operating ML model serving systems or ML platforms in production


• Deep understanding of distributed systems, APIs, and microservices architecture


• Strong track record of working across team boundaries to deliver complex, cross-functional projects


• Excellent communication skills — able to work fluently with Data Scientists, Machine Learning Engineers, and Data Engineers


• A collaborative, low-ego approach with strong technical judgment

Nice to Have

• Experience with ML frameworks and serving tools


• Familiarity with feature stores, model registries, or MLOps tooling


• Background in e-commerce, subscription, or personalization domains