Senior Director of Machine Learning Engineering

• 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.

What you'll bring

• 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.