Machine Learning Engineering Manager - Personalisation

About this role

Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do.

Our Engineering teams tackle complex technical challenges across a global, three-sided marketplace, building and scaling systems that serve millions of customers, riders and partners every day. We’re looking for a Machine Learning Engineering Manager to join us in London on a hybrid basis (3 days a week). In this role, you’ll lead the team that owns intelligence across our ordering experience, from store page and item selection through cart, upsell and checkout, plus bundling.

Personalisation is one of the biggest levers we have to drive value to our customers, and your team will make the app feel less like a generic catalogue and more like one that understands what each customer is trying to do. What you’ll be doing Lead and develop a high-performing team of around 12 Machine Learning Engineers, distributed across multiple pods and spread across London, Finland and Berlin, fostering a culture of technical excellence, ownership and rapid experimentation.

Own and evolve the ranking, recommendation and personalisation systems across ordering surfaces, helping customers find the right items quickly as our selection grows. Set the technical vision for the team’s ML ecosystem, balancing near-term product goals with longer-term bets on areas like on areas across recommendations to agentic approaches, and intent-aware experiences that adapt dynamically to how customers browse and act.

Lead the unification of our recommendation models across brands (Deliveroo, Wolt and DoorDash) and markets, working out how to power personalisation at global scale while keeping it feeling hyperlocal, and how to balance reusable core models against surface-specific adaptations without ending up with a sprawl of models. Partner with Product, Data Science and neighbouring engineering teams to identify high-impact opportunities, define roadmaps and translate ambiguous business problems into ML solutions.