Machine Learning Engineer - Data Science
Porto
Farfetch - Technology – Engineering /
Permanent /
Hybrid
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.
TECHNOLOGY
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.
PORTO
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.
THE ROLE
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 you will work with other Machine Learning Engineers, Data Scientists, Product Manager 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 getting data science solutions ready for use and integrating them into our internal business products or operational systems.
WHAT YOU'LL DO
- Work with Machine Learning Engineers, Data Scientists and Software Engineers to create production-quality Machine Learning pipelines and solutions with an emphasis on Performance, Scalability, Reliability and Maintainability
- Build components and libraries that will improve existing solutions and improve the delivery of new ones
- Continuously optimize machine learning models and pipelines to improve performance and efficiency, and develop monitoring strategies to ensure reliability
- Surface models and their outputs through the construction of ETLs and APIs.
WHO YOU ARE
- Experience implementing end-to-end data products with Data Engineering and Machine Learning components - from the system design to construction, deployment, orchestration and monitoring
- Experience writing production quality code in Python 3, including, for example, dependency management and testing frameworks
- Familiarity with the engineering aspects of some of popular Machine Learning practices, libraries and platforms (e.g. MLflow, Databricks, Spark, MLlib, PyTorch, Numpy, Pandas, TensorFlow and Scikit-learn among others).
