Senior Machine Learning Engineer
DeliverooLondon - The River Building HQPosted 27 March 2026
Job Description
Senior Machine Learning Engineer
SENIOR 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 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 Senior Machine Learning Engineer to join our London office as part of a Global DoorDash engineering team (working hybrid, 3 days in the office).
You’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the search and personalization experiences at the heart of our fast-growing New Verticals business, encompassing grocery, convenience, and retail delivery.
WHAT YOU’LL DO
You’ll be joining the New Verticals Personalisation team (Search & Recommendations). This team sits at the heart of how customers discover and shop across grocery, convenience, and retail categories, and is a key driver of the growth of Deliveroo's expanding New Verticals business. Working across the wider DoorDash engineering team, you’ll help shape intelligent, consumer-facing experiences that solve real customer problems at scale. This role spans a broad range of cutting-edge machine learning challenges, including modern recommendation and ranking systems, real-time decisioning as well as GenAI-powered experiences.
Here’s what your day-to-day might look like:
- Design and build production-grade machine learning and optimisation systems that power Deliveroo's New Verticals search and recommendations at scale.
- Partner with engineering and product leaders to help shape the product roadmap leveraging ML.
- Provide technical leadership across multiple product areas, identifying and prioritising opportunities for high-impact algorithmic improvements.
- Mentor junior team members, and lead cross functional pods to generate collective impact across the business.
WHAT YOU’LL NEED TO THRIVE
Our ideal candidate will bring strong expertise in some of these areas and curiosity to grow in others:
- Proven track record of designing and shipping impactful algorithms that deliver measurable business outcomes in a production environment.
- Deep expertise in applied ML for Search/NLP/IR/RecSys - both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms, LLMs, and causal inference techniques are a plus.
- Machine learning background in Python; experience with PyTorch or TensorFlow preferred.
- Ability to think holistically about business problems, moving beyond technical implementation to influence long-term strategy and roadmap.
- Excellent communication and stakeholder management skills, with the ability to influence both technical and non-technical leaders across the global organisation.
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.
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 colla ... (truncated, view full listing at source)
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