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Deliveroo

Staff Machine Learning Engineer - Ads Bidding

  • Hybrid
  • 6+ years

Salary

Not stated

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Description

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.

Staff Machine Learning Engineer – Ads Bidding

The Team

The Ads ML team builds and maintains the machine learning models powering Deliveroo's advertising product — connecting consumers with relevant ads and helping merchants, grocers, and FMCG brands grow through the platform. The UK team is ~11 MLEs across ads and promotions, and collaborates closely with the US team (meetings capped at 4pm UK to protect reasonable hours).

The Problem

Deliveroo is migrating ads from cost-per-click (CPC) to cost-per-order (CPO) — merchants pay when an ad drives an order, not when it's clicked. This is a genuinely open, cutting-edge problem: very few companies have solved it, and there's no established playbook to follow. The exact approach is under active discussion and you'd be central to driving it.

Responsibilities

  • Own the design and execution of the CPC-to-CPO migration, including the core open question of whether the system stays CPO-facing externally while optimising CPC behind the scenes, or some hybrid of the two
  • Build and improve the bidding surface: goal-based automated bidding, Target CPO, optimised CPC, and Target ROAS
  • Own yield management tied to ROS targeting
  • Partner daily with the DoorDash US team to align on approach across markets
  • Set technical direction for the domain

Requirements

  • Expertise in ad economics, marketplace bidding, or auction theory
  • Direct working experience with optimised CPC, target CPO, goal-based automated bidding, and target ROAS
  • Strong self-driven leadership baseline — you'll be setting direction in an ambiguous space, not executing against an established one
  • 7+ years writing production code in Python; solid ML fundamentals in ranking, recommendation systems, or bidding/auction systems
  • A bias for simplicity and a focus on shipping work with measurable business impact
  • Strong cross-functional communication — you'll be working closely with Product, Commercial, and the wider DoorDash team

Benefits

  • 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.

Where you’d work

Part of the week in the office

You can work from

  • United Kingdom

About the company

Deliveroo

Office in London, United Kingdom

Also hiring in Belgium, Milan, Italy, Paris, France and 2 more places

1 of their 65 open roles is remote

Your chances

Worth a look before you spend an evening tailoring a CV for it.

  • 19 checks run
  • 2 red flags

Still hiring?

13 checks

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6 checks

2 red flags

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  • Must-haves
Details12 facts · Role, Location, Compensation
Tech stack
  • Python
Seniority
Staff
Equity
Equity offered
Specialty
ML
Region
United Kingdom
Pay period
Annual
Show 6 more factsShow less

Role

Category
Data & Analytics
Specialty
ML
Seniority
Staff
Experience
7+ years
Tech stack
  • Python

Location

Work model
Hybrid
Region
United Kingdom
Office
  • London, United Kingdom
Remote from
  • United Kingdom

Compensation

Salary
Salary by agreement
Pay period
Annual
Equity
Equity offered

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