Machine Learning Engineer
DeliverooLondon - The River Building HQPosted 30 April 2026
Tech Stack
Job Description
Machine Learning Engineer
MACHINE LEARNING ENGINEER
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.
From real-time logistics to resilient infrastructure and marketplace optimisation, we design, build and operate technology that powers Deliveroo’s growth at scale.
We’re looking for several Machine Learning Engineers to join our London (hybrid) teams.
WHAT YOU’LL BE DOING
In this role, you’ll help us develop the algorithmic and machine-learning systems that power Deliveroo, making automated decisions at massive scale across our three-sided marketplace. Based on the team you join and the vacancies we currently have, here's what your role may look like:
1. Rider Pricing – improving our next-generation rider pricing models that algorithmically set fees for riders for the order assignments we make.
2. Delivery – working on models that predict delivery times across our platform, powering both the consumer experience and our dispatch systems.
3. New Verticals – developing recommendation and catalogue-quality models that help customers discover the right grocery and retail items while supporting growth and efficiency.
Irrespective of the team, you will:
- Design and build sophisticated machine learning models to solve fuzzy business problems
- Productionise end-to-end ML solutions, moving from initial concept to shipping code that drives measurable impact on our global marketplace.
- Collaborate in cross-functional squads alongside software engineers, data scientists, and product managers to build intelligent decision-making machines.
WHAT YOU’LL NEED TO THRIVE
Our ideal candidate will bring strong expertise in some of these areas and curiosity to grow in others:
- Experience applying algorithmic solutions to complex business problems at scale.
- Deep understanding of machine learning fundamentals and a proven ability to know when and how to apply them effectively.
- Demonstrated ownership of end-to-end model productionisation, with a strong background in software engineering principles.
- A bias for simplicity and a focus on achieving tangible business impact over theoretical complexity.
- Exceptional programming and statistical skills, with the ability to translate ambiguous objectives into technical reality.
WHY JOIN US?
At Deliveroo, you’ll do work that matters—solving real-world problems in a three-sided marketplace that’s constantly evolving. We’re food lovers, problem solvers, community builders and more, brought together by a shared drive to make things better. Working here you can expect to:
🔧 Solve meaningful problems at real scale
Work on a complex, always-on marketplace that impacts millions every day.
🌱 See your impact, fast
Ship, test and improve ideas quickly in a low-hierarchy, high-ownership environment.
🧠 Grow through challenge and ownership
Take on big, ambiguous problems and accelerate your career with strong support.
🌎 A culture built for builders
High standards, collaboration, flexible working and continuous learning.
💰 Share in the success you help create
Competitive salary and equity options, so you’re rewarded for the impact you make.
➡️ Want a deeper look at how we build? Check out our Tech Blog https://deliveroo.engineering/.
We aim to create a fair process that lets your skills shine—our interview typically includes 3-4 stages. Explore our Engineering Interview Guide here https://careers.deliveroo.co.uk/interview-guide-for-engineers/ to help you feel confident at each stage.
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 ... (truncated, view full listing at source)
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