Senior Director of Machine Learning Engineering

Lead a globally distributed organization of 25-30 engineers, data scientists, and ML practitioners across Berlin, Warsaw, NYC, Boulder, and Toronto. You'll set technical strategy across ML, backend, and data disciplines, owning benefit recommendation, personalization, customer lifetime-value forecasting, and pricing and subscription infrastructure at scale.

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.
• 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 need

• Range across ML and backend engineering with 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.
• Distributed systems and backend depth, including scaling backend services and data pipelines in revenue-sensitive, high-throughput environments.
• AI-native leadership, with a track record of building or scaling AI-native engineering practices.
• Commercial and pricing domain fluency, including benefit optimization, lifetime-value forecasting, and pricing elasticity.
• 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.

Nice to have

• Causal inference or uplift modeling experience.
• Subscription or billing experience.

What they offer

• Global collaboration across hubs in Berlin, Warsaw, NYC, Boulder, and Toronto.
• Flexible hours, Work From Home/Abroad options, and home office setup budget.
• Company pension scheme, discounted meal kits, and Urban Sports Club/John Reed memberships.
• Childcare support and monthly Deutschlandticket allowance.
• Access to internal trainings and central L&D budget.
• Modern office in Berlin-Kreuzberg with social events and team outings.