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.
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 a Senior Machine Learning Engineer to join our London team. In this role, you'll help shape the decision-making systems that directly impact customer experience, trust, and fairness at Deliveroo. This role is based in our London office with a hybrid setup, typically 3 days in the office.
What You'll Be Doing
You'll be joining the Care team. Care is an area with significant opportunity and real customer impact; you'll take ownership of existing models and help define strong ML foundations in spaces that are still evolving, from LLM-powered automation to customer compensation logic.
Here's what your day-to-day might look like:
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Own the full ML lifecycle: Lead the design, development, and productionisation of machine learning models used in customer support and high-stakes decision systems.
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Architect for reliability: Build robust monitoring, evaluation, and alerting frameworks to detect model underperformance, drift, or unexpected behaviour in real-time.
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Collaborate on product strategy: Partner closely with Product Managers to turn ambiguous customer problems into robust, scalable ML solutions.
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Drive technical excellence: Provide technical leadership in areas with unclear ownership, setting the gold standard for ML quality, reliability, and maintainability.
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Bridge science and engineering: Work with data scientists and engineers across the global organisation to embed models into high-performance, scalable systems.
What You'll Need to Thrive
Essential
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Significant experience as an ML Engineer or Data Scientist, with a proven ability to write high-quality production code in Python.
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Demonstrated ownership of productionising Generative AI workstreams or Agentic AI projects, including a deep understanding of LLMs, transformers, and fine-tuning.
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Proven ability to deploy and manage models using modern infrastructure tools such as Docker, Kubernetes, and CircleCI.
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Strong foundational knowledge of traditional ML and evaluation techniques, balanced with a bias for simplicity and measurable business impact.
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A collaborative mindset with experience mentoring peers and a drive to solve complex, real-world problems at scale.
Nice to Have
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Experience with evaluation harnesses and frameworks specifically designed for Generative AI.
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Familiarity with observability, monitoring, and safety techniques for deployed GenAI systems.
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Experience working with strongly typed languages, such as Go.