Opportunity at
DeliverooSenior Machine Learning Engineer - Recommendations
This senior machine learning engineering role is based in London and sits within the Consumer organisation of DoorDash's global engineering group, alongside colleagues from Deliveroo and…
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About This Role
This senior machine learning engineering role is based in London and sits within the Consumer organisation of DoorDash's global engineering group, alongside colleagues from Deliveroo and Wolt. The team works on recommendation problems that help customers find relevant content, such as restaurants, dishes and items. The listing describes hybrid working, with three days in the office each week. The engineer builds and refines recommendation, ranking and retrieval models. The work…
Job Description
This senior machine learning engineering role is based in London and sits within the Consumer organisation of DoorDash's global engineering group, alongside colleagues from Deliveroo and Wolt. The team works on recommendation problems that help customers find relevant content, such as restaurants, dishes and items. The listing describes hybrid working, with three days in the office each week.
The engineer builds and refines recommendation, ranking and retrieval models. The work spans framing each applied ML challenge, examining data, creating models, specifying offline assessments, running experiments and tracking production results. A recurring challenge is weighing relevance against wider product considerations such as variety of options, stock availability, commercial limits and shifting user preferences.
The post pairs the engineer with product managers, analysts and fellow ML and software engineers to turn research into dependable customer features. It also means pushing the broader applied science practice forward through peer technical reviews, knowledge sharing and well-designed trials. Evaluating newer methods is part of the remit, with adoption only when they clearly improve quality, robustness or efficiency.
Candidates should have considerable practical experience applying ML to real problems and getting models into production. A background in recommender systems, ranking, retrieval or personalisation is important. Proficiency with Python, familiarity with common model-building toolkits and large-scale data handling, and a disciplined approach to evaluation are needed. A PhD with applied research experience is also welcome.
Responsibilities
- Design, build and enhance models for recommendations, ranking and retrieval of restaurants, dishes and content
- Take ML problems from framing through to production monitoring
- Balance relevance with variety, availability and commercial constraints
- Partner with product managers, analysts and fellow engineers to get customer features shipped
- Evaluate newer methods and adopt them when they clearly help
- Raise standards through technical reviews and knowledge sharing
Requirements
- Practical know-how with ML in real settings, including taking models live
- Experience with recommendations, ranking, retrieval or personalisation
- Skill in shaping vague problems into scoped ML solutions with measurable outcomes
- Strong Python, plus a working grasp of mainstream toolkits and distributed data processing
- Rigorous evaluation capability across offline metrics, experiment design and online results
- Clear communication with cross-functional partners
Qualifications
- PhD with applied research experience, welcome alongside practical experience
Core Skills
Benefits
- Competitive salary and equity options
Frequently Asked Questions
Answers are based only on the employer’s listing; where it doesn’t say, neither do we.
Where is the role based and what is the working pattern?
The posting places the role in London with hybrid working, three office days each week.
Is a PhD required?
No. A PhD is welcomed, but substantial hands-on experience is the main requirement.
Which skills matter most?
Python, modern ML frameworks, large-scale data processing, and rigorous offline and online evaluation.
Is pay stated?
No figure is given. The posting mentions competitive salary and equity options.
Sample Interview Questions
AI-generated questions tailored to this specific role — a preview of the full practice set.
