About LystLyst is a global fashion shopping platform founded in London in 2010 and catering to over 160M shoppers per year. We offer our customers the largest assortment of premium &amp; luxury fashion products in one place, curating pieces from 27,000 of the worlds leading brands and stores. In 2025, Lyst joined Zozo, operators of Zozotown, the leading fashion e-commerce platform in Japan. This partnership marks a bold new era for Lyst, as we accelerate our vision and work together to transform the future of fashion shopping through AI and technology.At Lyst, we obsess over the customer, providing a search &amp; discovery experience which offers inspiration, fulfilment, and personalisation. We believe that fashion is amazing but shopping for fashion often isnt, and use our technology, data and creativity to bring more joy, greater choice and fewer fails. Our mission is to help fashion shoppers make better decisions and help fashion partners find better audiences as the category-leading destination for every fashion shopper.RoleWere looking for a commercially minded and technically strong Machine Learning Engineer to join our Product Listings team—focused on unlocking smarter, more scalable advertising through automation and optimisation.This is a hybrid role combining deep data science expertise with production-grade ML engineering. You’ll be a driving force behind our performance marketing automation: designing experiments, building predictive models, and deploying them at scale to help optimise product bidding and maximise profit across platforms like Google Shopping.Expect to work across the full ML lifecycle—from exploring large, messy datasets and engineering features, to evaluating models offline and running experiments to validate impact. At the same time, you’ll build, deploy and monitor models in production: setting up retraining workflows, pipeline orchestration, and performance alerts. You will be supported in this by your tech lead and colleagues from the wider Data Science chapter at Lyst.We work mainly in Python using all the standard ML toolkits and frameworks (e.g. SKLearn, Tensorflow, Pytorch), and run our ML code in the AWS environment using Sagemaker where possible. We have a strong preference for clean, documented, well tested and reviewed code and have tooling and a culture to support this.This is a hands-on, high-impact role that blends research, experimentation and engineering, all tied to clear business outcomes. Youll collaborate closely with marketers, data analysts and engineers, and play a key part in shaping the future of how we scale our advertising.Responsibilities<ul> <li>Productionise Machine Learning models into robust, maintainable systems—including retraining pipelines, monitoring, and performance alerting</li> <li>Build and evaluate ML models that support smarter paid marketing—e.g. product-level ROAS prediction, lifecycle-aware bidding, and grouping optimisation</li> <li>Explore large-scale product and performance datasets to uncover insights, test hypotheses, and identify features that drive business value</li> <li>Work on all parts of the data science workflow: from data cleaning and exploratory analysis to offline model benchmarking and validation</li> <li>Collaborate with marketers to reduce manual overhead in campaign management while improving ad efficiency and profitability</li> <li>Improve our ad feed infrastructure to ensure product attributes, pricing and availability are optimised for campaign performance</li> <li>Contribute as needed to product data initiatives (e.g. classification, deduplication), especially where they impact ad quality or model input</li> </ul><ul> <li>3+ years of experience building and deploying ML models in production</li> <li>Strong Python and SQL skills, with a solid understanding of data wrangling, feature engineering and model evaluation</li> <li>Deep understanding of the data science process—comfortable with exploratory analysis, statistical testing, and model comparison techniques</li> <li>Experience with structured prediction problems (e.g. regression, classification, ranking) using real-world, often messy data</li> <li>Familiarity with advertising systems (Google Shopping, PMAX, etc.) and marketing metrics like ROAS, profit, and conversion is a strong advantage</li> <li>Experience productionising ML models—setting up training pipelines, versioning, monitoring and retraining</li> </ul>

Lyst

Senior Machine Learning Engineer

Lyst

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