Deliveroo - London - The River Building HQ - Global - Engineering & Technical
Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do. Our Global 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 Engineer to join our London office as part of a Global DoorDash Engineering team (working hybrid, 3 days in the office).
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.
You’ll be working on delivery-time prediction, owning the models that power the ETA a customer sees and the ETA-related signals used by our dispatcher when automatically allocating riders to orders.
You will:
Design and improve ETA and delivery-time prediction models that power the consumer-facing experience across Deliveroo.
Build models used by our dispatcher, helping inform the automatic allocation of riders to orders.
Improve prediction quality and reliability across different markets, order types and delivery conditions.
Productionise end-to-end ML solutions, moving from initial concept to shipping code that drives measurable impact across our marketplace.
Collaborate in cross-functional squads alongside software engineers, data scientists and product managers to improve how delivery-time predictions are used across the platform.
Our ideal candidate will bring strong expertise in some of these areas and curiosity to grow in others:
Minimum 3+ years of experience as an ML Engineer or Data Scientist, with a proven ability to write high-quality production code in Python
Experience applying machine learning and algorithmic solutions to complex business problems in production.
Strong understanding of machine learning fundamentals and the ability to select and apply appropriate modelling techniques.
Experience building predictive models, particularly around forecasting, time estimation or other real-time decision-making problems.
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