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London, UK, United Kingdom
Hybrid
Company Description
Depop is a peer-to-peer circular fashion marketplace where anyone can buy, sell and discover secondhand fashion. Our mission is simple: to make fashion circular by making secondhand as exciting and rewarding as buying new.
Founded in 2011, Depop’s diverse community has helped move resale into the mainstream, where buying secondhand is no longer an alternative, but how people of different ages now engage with fashion. Today, more than 56 million registered users come to Depop to find great value, express their own personal style and give clothes a longer life. We believe that everything you want already exists, and our role is to help people discover it.
Powered by a team of over 500 people, our company is headquartered in London, with offices in New York. In 2021, Depop became a wholly-owned subsidiary of Etsy - the global marketplace for unique and creative goods - and continues to operate as a standalone company. For more information, visit www.depop.com
We aim 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.
AI Disclosure: We use AI tools (Google Gemini) to help our team source and review applications for roles with a high volume of applications. These tools assist our recruiters in identifying great talent but do not replace human decision-making. At Depop, every hiring decision is made by a human.
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.
Depop is looking for a Machine Learning Engineer to join the Ranking 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 ranking across key surfaces of the Depop app, including search results and recommendations.
The Ranking team develops learning-to-rank models that personalise the ordering of items for millions of users every day. These models are deployed for real-time inference and integrated across multiple services in the Depop platform.
As a Senior ML Engineer in this team, you will play a key role in building the infrastructure and systems required to train, deploy, and operate scalable ranking models in production.
You will:
Design and implement pipelines for training, evaluating, deploying, and monitoring learning-to-rank models.
Work closely with ML Scientists to productionise ranking models, improving reliability, latency, and observability.
Build and optimise real-time model serving systems that deliver personalised rankings across the app.
Partner with backend and product teams to define integration requirements and coordinate deployment of ranking services.
Help extend the ML infrastructure for ranking systems in collaboration with the MLOps team, including:
Reproducible model training workflows
CI/CD pipelines for model deployment
Real-time and batch model serving
Online/offline feature consistency through the feature store
Monitoring and alerting for production models
Maintain high standards for operational excellence, including testing, monitoring, maintenance, and incident response.
Contribute to a strong engineering culture focused on scalability, experimentation, and measurable impact.
Proven experience building and deploying machine learning pipelines in production environments.
Experience working with ranking, recommendation, or retrieval systems.
Strong understanding of machine learning workflows, from experimentation to production deployment.
Experience designing and operating systems in modern cloud environments (e.g. AWS or GCP).
Strong ownership mindset with the ability to work independently in a fast-moving environment.
Excellent communication skills and the ability to collaborate with cross-functional stakeholders.
Python
Machine learning frameworks (e.g. PyTorch, TensorFlow, scikit-learn)
ML / MLOps tooling (e.g. SageMaker, MLflow, TFServing)
Spark and Databricks
AWS services (e.g. IAM, S3, Redis, ECS)
CI/CD tooling and best practices
Streaming and batch data systems (e.g. Kafka, Airflow, RabbitMQ)
Additional Information
Health + Mental Wellbeing
Work/Life Balance:
Family Life:
Learn + Grow:
Your Future:
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