Senior Director of Machine Learning Engineering

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, an...

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.

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.
Manage and develop Engineering Managers and Data Science Leads across disciplines and geographies, holding them accountable for team health, delivery, and engineering standards.

Range across ML and backend engineering. 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.
Proven leadership of globally distributed teams across multiple countries and time zones, without daily co-location. Comfortable with regular travel and bridging US and European hours.
Deep ML engineering expertise, including feature engineering, training/serving infrastructure, experimentation, and MLOps. Causal inference or uplift modeling experience is a plus.
Distributed systems and backend depth, including scaling backend services and data pipelines in revenue-sensitive, high-throughput environments. Subscription or billing experience is a plus.
AI-native leadership, with a track record of building or scaling AI-native engineering practices, not just adopting AI tools.
Commercial and pricing domain fluency, or strong aptitude to build it quickly, including benefit optimization, lifetime-value forecasting, and pricing elasticity.
Proven leadership at scale: 12+ years in software/ML engineering, with 5+ years managing Engineering Managers across more than one technical discipline and geography.
Operational excellence mindset, with strong grounding in SRE and MLOps practices for systems with direct financial impact.
Strong cross-functional communication, translating ML, data, and backend concepts into business terms and vice versa.
Low ego, high ownership, and a bias toward clarity over complexity.