Senior Director of Machine Learning Engineering

Overview

The CVO Tribe CVO owns two of HelloFresh's largest economic levers: benefit optimization and pricing . The tribe uses machine learning, personalization, and lifetime-value prediction to replace manual, rules-based decisioning with data-driven systems. The engineering org is globally distributed across Berlin, Warsaw, NYC, Boulder, and Toronto, and includes Frontend, Backend, Data, and ML Engineeri

What you'll do

• The CVO Tribe CVO owns two of HelloFresh's largest economic levers: benefit optimization and pricing.
• The tribe uses machine learning, personalization, and lifetime-value prediction to replace manual, rules-based decisioning with data-driven systems.
• The engineering org is globally distributed across Berlin, Warsaw, NYC, Boulder, and Toronto, and includes Frontend, Backend, Data, and ML Engineering, working closely with embedded Data Scientists.
• The work spans a genuinely mixed engineering profile: ML-heavy systems for benefit recommendation, personalization, and customer lifetime-value forecasting, alongside backend and distributed-systems work powering pricing infrastructure and subscription products at scale.
• As Senior Director, based in Berlin, you'll lead this full spectrum, setting technical strategy across ML, backend, and data disciplines and across time zones, without relying on daily co-location.
• What you'll do Lead an organization of 25-30 engineers, data scientists, and ML practitioners across Berlin, Warsaw, NYC, Boulder, and Toronto, through a layer of Engineering Managers and Staff Engineers reporting into you.
• Own ML strategy for benefit recommendation, personalization, and customer lifetime-value forecasting, as well as backend and distributed-systems strategy for pricing and subscription infrastructure.
• Drive the transformation of ways of working toward fully GenAI-native, cross-functional product teams, building on teams that already ship the majority of their code with AI assistance.
• Own reliability and operational excellence across both ML and backend systems: observability from model output through to customer-facing delivery, SLOs/SLIs, incident management, and MLOps practices such as retraining, rollback, and experiment tracking.
• Partner with Product, Data Science, Marketing, Finance, and adjacent engineering teams to align engineering priorities with business outcomes.

Requirements

• Practical experience with Tech.
• Experience level: 12+ yrs.
• Strong written and verbal communication in English.
• Comfortable working on-site in Berlin, Berlin.

Skills