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A global e-commerce platform in the UK is seeking a Machine Learning Engineer to join their Data Science Product Matching team. You will develop and optimize ML pipelines, ensuring performance, scalability, and reliability. The ideal candidate has experience with Python, ML libraries, and end-to-end data products. This role offers the opportunity to shape the technical direction in a dynamic team environment.
Farfetch is a leading global marketplace for the luxury fashion industry. The Farfetch Marketplace connects customers in over 190 countries and territories with items from more than 50 countries and over 1,400 of the world's best brands, boutiques, and department stores, delivering a truly unique shopping experience and access to the most extensive selection of luxury on a global marketplace.
We're on a mission to build end-to-end products and technology that powers an incredible e-commerce experience for luxury customers everywhere, understanding the motivations and needs of our customers and partners, to designing and testing hypotheses, to creating industry-leading experiences for luxury customers.
Our office is near Porto, in the north of Portugal, and is located in a vibrant business hub. It offers a dynamic and welcoming environment where our employees can connect and network with a large community of tech professionals.
We are looking for a Machine Learning Engineer to join our Data Science Product Matching team: the main objective of Product Matching is to determine which of our competitors are selling the same products as us and what these products are, to support the definition of competitiveness strategies and enable a more efficient reaction. You will report to a Software Engineer Lead and work with other Machine Learning Engineers, Data Scientists, Product Managers, and other technical teams, both here in Portugal and across our other locations, shaping the technical direction of a critically important part of Farfetch. Your role will involve preparing data science solutions for use and integrating them into our internal business products or operational systems.