Senior Machine Learning Engineer, Recommendations

Company Description

Depop is the community-powered circular fashion marketplace where anyone can buy, sell and discover desirable secondhand fashion. With a community of over 35 million users, Depop is on a mission to make fashion circular, redefining fashion consumption. Founded in 2011, the company is headquartered in London, with offices in New York and Manchester, and in 2021 became a wholly-owned subsidiary of Etsy. Find out more at www.depop.com

Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome, no matter who they are or where they’re from. Just as our platform connects people globally, we believe our workplace should reflect the diversity of the communities we serve. We thrive on the power of different perspectives and experiences, knowing they drive innovation and bring us closer to our users. We’re proud to be an equal opportunity employer, providing employment opportunities without regard to age, ethnicity, religion or belief, gender identity, sex, sexual orientation, disability, pregnancy or maternity, marriage and civil partnership, or any other protected status. We’re continuously evolving our recruitment processes to ensure fairness and are open to accommodating any needs you might have.

If, due to a disability, you need adjustments to complete the application, please let us know by sending an email with your name, the role to which you would like to apply, and the type of support you need to complete the application to adjustments@depop.com.

For any other non-disability related questions, please reach out to our Talent Partners.

Role

Depop is looking for a Machine Learning Engineer to join the Recommendations team in the UK. You will work alongside ML Scientists, Backend Engineers, MLOps and other ML Engineers to build, deploy, maintain, and monitor the machine learning systems that power personalised product recommendations across key surfaces across the app.

Responsibilities

You will:

• Design and implement pipelines for training, evaluating, deploying, and monitoring retrieval models
• Work closely with ML Scientists to productionise recommendation models, improving reliability, latency, and observability
• Build and optimise embedding generation and recommendations serving
• Partner with backend and product teams to define integration requirements and coordinate deployments of recommendation services
• Help extend the recommendations ML infrastructure in collaboration with MLOps, including:
• Reproducible training workflows
• CI/CD for model deployment
• Real-time and batch model serving