ML Engineer Intern – Build Production- Models

Why Deliveroo

Deliveroo is a great place to start your career as a Machine Learning Engineer because you’ll work on real problems, at real scale, alongside some of the brightest technical minds working in applied machine learning today.

We have a brilliant ML community with 50 MLEs based in London alongside a further 200 Data Scientists, Data Analysts and Analytics Engineers. Here, you’ll be embedded in a product team, paired with experienced engineers, supported by a strong ML community, and responsible for owning and launching machine learning models in production. To do this, you’ll have the support of peers, mentors, and a manager who shares best practices, reviews work, and actively mentors early‑career talent.

With Deliveroo now a part of Door Dash, you will also become a part of Door Dash’s global Engineering org, which will help you to meet even more colleagues around the world.

The problems we work on are genuinely complex. We operate a three‑sided marketplace, handle large volumes of real‑time data, and deploy machine learning models that directly affect how millions of people discover food, place orders, get deliveries, and receive support. Your work will be grounded in real‑world constraints and trade‑offs, not toy datasets or academic exercises.

If you’re interested in applied machine learning that ships to production, has measurable impact, and is built collaboratively rather than in isolation, this is a strong place to learn fast.

As a Machine Learning Engineer intern at Deliveroo, you’ll work on a well‑scoped machine learning project within a product or platform team. The goal of the internship is not independent research. It is to help you learn how machine learning systems are designed, built, evaluated, and deployed in a real production environment.

Applicants must be authorised to work in the U.K. to be considered for this position.

The 2026 internship programme will be hosted in person at Deliveroo’s London headquarters at Cannon Bridge House, with requirements to be in office 5 days a week. This opportunity will be a 12‑24 week summer placement.

Depending on the team you join, you might work on:

• Consumer & Marketplace – Ranking, search, and recommendation systems that decide which restaurants, dishes, grocery and retail items to show; personalisation and pricing models that optimise fees, promotions, and loyalty while balancing growth and profitability.

• Delivery & Logistics – Real‑time decision systems that assign riders to orders, route and schedule deliveries, and predict travel and prep times; forecasting and simulation models that plan demand, rider supply, staffing, and financial performance.

• Restaurants, Grocery & Retail (New Verticals) – Models that grow and optimise our merchant ecosystem, from catalogue and inventory quality to upsell and cross‑sell, helping partners improve menus, pricing, photography, and overall presence on Deliveroo.

• Advertising & Monetisation – Ad ranking, relevance, bidding, and budget‑optimisation models that maximise advertiser performance and marketplace revenue while preserving a great customer experience.

• Customer Care, Trust & Safety – LLM‑ and ML‑powered systems for customer support automation, sentiment and intent understanding, fair compensation and refund decisions, and fraud and abuse detection.

• ML & Agentic AI Platforms – Core ML and Agentic AI infrastructure: training, deployment, evaluation, experimentation, and safety tooling used by product teams across Deliveroo to build, monitor, and improve production ML and GenAI systems.

• Hands‑on experience working on production ML systems

• Close mentorship from experienced Machine Learning Engineers

• Exposure to how ML is applied at scale in a fast‑moving product environment

• The chance to:

  • own a piece of a defined project and present your work to the team

  • contribute and execute on research ideas that can be applied and used to improve product experiences

  • collect, analyse, and synthesise findings from data and use these insights to build relevant ML models

  • write clean, efficient, and sustainable code

If the internship goes well, there is the potential…