FARFETCH

Machine Learning Engineer - Search and Recommendat...

Job description

FARFETCH exists for the love of fashion. Our mission is to be the global platform for luxury fashion, connecting creators, curators and consumers.

We're a positive platform for good, bringing together an incredible creative community made up by our people, our partners and our customers. This community is at the heart of our business success. We welcome differences, empower individuality and celebrate diverse skills and perspectives, creating an inclusive environment for everyone. We are FARFETCH for All.

TECHNOLOGY

We're on a mission to build the technology that powers the global platform for luxury fashion. We operate a modular end-to-end technology platform purpose-built to connect the luxury fashion ecosystem worldwide, addressing complex challenges and enjoying it. We're empowered to break traditions and revolutionise, with the freedom and autonomy to make a difference for our customers all over the world.

PORTO

Our Porto office is located in Portugal's vibrant second city, known for its history and its creative yet cosy environment. From Account Management to Technology and Product, whatever your skills are, you'll find your fit here. You can have an informal meeting in the treehouse or play the piano in your lunch break!

THE ROLE

We are seeking a highly skilled and motivated Machine Learning Engineer to join our team in building an advanced personalization platform for online services, leveraging large-scale data and state-of-the-art Machine Learning techniques. You will work within a dynamic, interdisciplinary team comprising software engineers, data scientists, and other machine learning engineers, reporting directly to the engineering leadership. Your role will focus on implementing and optimising machine learning models for search, recommendations, and hyper-personalization, with a strong emphasis on information retrieval and recommendation systems. The platform serves millions of requests daily, powered by Python microservices and processes billions of data points to hyper-personalise the customer experience.

What You'll Do

  • Work with technical partners to implement the end-to-end architecture;
  • Surface the team's output through the construction of ETLs, APIs and web interfaces;
  • Design model serving solutions and develop machine learning-based applications, services, and APIs so as to productionize machine learning models;
  • Partner with the Data Scientists and Software Engineers to provide an end-to-end solution for machine learning-based projects;
  • Continuously optimize machine learning models and pipelines to improve performance and efficiency, and develop monitoring strategies to ensure reliability;
  • Foster the technological evolution of services and improve their end-to-end quality attributes.

Who You Are

  • BSc, MSc, or PhD in relevant fields such as Computer Science, Machine Learning, Data Science, or related disciplines
  • 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, 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);
  • Experience Continuous Integration & Continuous Deployment processes and platforms, software design patterns and APIs;
  • Experience with Kubernetes;
  • A person that enjoys staying on top of all the best practices and tools of modern software engineering, while being an advocate of code quality and continuous improvement.

We are seeking a highly skilled and motivated Machine Learning Engineer to join our team in building an advanced personalization platform for online services, leveraging large-scale data and state-of-the-art Machine Learning techniques.

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