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# Senior Machine Learning Engineer, Search & Recommendations

- **Company:** DoorDash, Inc.
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Big Data, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Machine Learning Operations
- **Published:** October 10, 2026
- **Apply:** https://www.totaljobs.com/job/senior-machine-learning-engineer/doordash-job108119257

## About the Role

* You have substantial hands-on experience applying machine learning to real-world problems and a track record of bringing models from development into production; a PhD with relevant applied research experience is equally welcome.
 * You have experience with recommendation systems, ranking, retrieval, personalisation, or closely related ML problems.
 * You can independently turn an ambiguous customer or product problem into a well-scoped ML approach, make sound trade-offs and drive it to a measurable outcome.
 * You are proficient in Python and experienced with modern ML frameworks and large-scale data processing.
 * You understand how to evaluate ML systems rigorously, including offline metrics, experiment design and interpreting online results.
 * You communicate complex technical ideas clearly and work effectively with cross-functional partners.

## Description

* Design, develop and improve recommendation, ranking and retrieval models that surface relevant restaurants, dishes, items and content to customers.
 * Own applied ML problems end to end: frame the problem, analyze data, develop models, define offline evaluation, run experiments and monitor production performance.
 * Develop methods that balance relevance with product and customer needs, such as diversity, availability, business constraints and changing user intent.
 * Collaborate closely with Software Engineers, ML Engineers, Product Managers and Analysts to turn scientific insights into reliable customer-facing products.
 * Evaluate and apply state-of-the-art ML methods where they meaningfully improve recommendation quality, robustness or efficiency.
 * Contribute to a high bar for applied-science practice through technical reviews, knowledge sharing and thoughtful experimentation.