Senior Director of Machine Learning Engineering

<h2><strong>The CVO Tribe</strong></h2> <p>CVO owns two of HelloFresh's largest economic levers: <strong>benefit optimization</strong> and <strong>pricing</strong>. The tribe uses machine learning, personalization, and lifetime-value prediction to replace manual, rules-based decisioning with data-driven systems.</p> <p>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.</p> <p>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.</p> <h2><strong>What you'll do</strong></h2> <ul> <li>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.</li> <li>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.</li> <li>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.</li> <li>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.</li> <li>Partner with Product, Data Science, Marketing, Finance, and adjacent engineering teams to align engineering priorities with business outcomes.</li> <li>Manage and develop Engineering Managers and Data Science Leads across disciplines and geographies, holding them accountable for team health, delivery, and engineering standards.</li> </ul> <h2><strong>What you'll bring</strong></h2> <ul> <li><strong>Range across ML and backend engineering.</strong> You don't need to be hands-on expert in both, but you need credibility in each: enough ML depth to set direction on production ML systems and partner effectively with Data Science, enough distributed-systems depth to be a trusted partner on pricing infrastructure and subscription products.</li> <li><strong>Proven leadership of globa