Senior Machine Learning Engineer - FARFETCH

UK London
25 May 2022
29 Jul 2022
Full Time
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 of 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.


We're a Data team that does it all: big data engineering, machine learning, and deep-dive analytics and insight. We're a diverse, global team who creates Data solutions to provide an unrivaled customer experience. Whether it's churning gigabytes of e-commerce data, using AI to recommend the latest trends, or understanding our customers better than anyone else, we use data to promote FARFETCH's growth.


Our office is located in Old Street, London's tech hub. With an open-plan space, ideal for collaborative working, an outdoor terrace for a team lunch, or a dedicated studio for a yoga class.


We are looking for a Senior Machine Learning Engineer to join a newly formed team whose mission is to unlock the potential of the ML community at Farfetch.

The ML ecosystem, both at Farfetch and in the wider community, is currently undergoing a rapid growth period. Whether that is feature stores that promote feature sharing and consistent online serving, vector databases that open up new search opportunities, to data processing engines that can seamlessly work on vast datasets. Simultaneously, the ecosystem relies on tools and techniques originally intended for software engineering that must be adapted to the needs of the ML community. Model development and deployment have different requirements of data access, version control, CI/CD, and container orchestration.

Making these tools available for teams is of course only the first step. The old adage of it's not the tool, but how you use it is as relevant as ever. Are we making efficient use of resources? Do we have the right level of visibility during training or inference? Are teams able to iterate solutions at the pace they require? How do the expectations of data science teams align with those of the platform maintainers? Some of these questions have straightforward practical solutions, while others can have profound implications on how we approach ML.

In this role, you will be part of a team that sits between platform and data science teams that ensures the needs of the ML community are met, whether that is by evaluating tools and their value, promoting best practices for model development, or even providing hands-on support.

You will be working on a team of Machine Learning Engineers and Data Scientists driving innovation and improvements to the ML ecosystem. You will work closely with the platform, data science, architecture, networking, security, and governance teams to make sure that the needs of the ML community are balanced with the requirements of all stakeholders.


    • Formulating best practices for tools, libraries, and processes
    • Evaluating new tooling
    • Use case gathering
    • Engaging with vendors
    • Proof of concepts
    • Providing hands-on engineering support for teams
    • Structuring and optimizing code
    • Integrating with tracking, logging, and metric tooling
    • Creating test harnesses
    • Interviews and onboarding


    • As an experienced Senior Machine Learning Engineer, you will be expected to maintain a holistic view of the challenges faced by the ML community and to leverage your experience in decision-making.
    • Strong understanding of Python
    • Extensive experience writing production-quality code, especially as it pertains to ML
    • Pandas, Numpy, Scikit, PyTorch, and various other DS/ML packages
    • Experience with cloud providers (Azure, GCP) and configuration tooling (Terraform)
    • Experience with containerization (Docker) and management (Kubernetes)
    • Experience with CI/CD tooling (Jenkins, ArgoCD)
    • Experience with monitoring and logging frameworks (Grafana, Prometheus, ELK)
    • Experience with data and experiment tracking (DVC and MLFlow)
    • Experience with orchestration/workflow management (Airflow)
    • Experience with Spark or better yet, Databricks


    • FARFETCH Equity plan and annual discretionary bonus
    • Flexible benefits - Private Medical Insurance, Dental Insurance, Gym Memberships, Pension scheme and more
    • Critical Illness Insurance and Life Assurance
    • Access to Unmind: an independent and completely confidential digital mental health platform
    • Flexible working environment and more!

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