Senior Machine Learning Engineer

Senior Machine Learning Engineer

Team: Engineering & Data

Location: Depop - London

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

Life is about creating. That's why we're home to over 30 million artists, stylists, designers, sneakerheads — and you? We're the community-powered, circular-minded marketplace changing the world of online fashion. Now it's time to get inspired at Depop.

Responsibilities

Job description

Role

Depop is looking for a dedicated Machine Learning Engineer to join our new Core ML team in the UK. You will work alongside ML Scientists, Backend Engineers, and MLOps to build, deploy, maintain, and monitor machine learning infrastructure, such as product matching pipelines, image embedding services, and lightweight classifier deployments, that support multiple product and marketing use cases across Depop.

Responsibilities

You will:

Design and implement pipelines for training, deploying, and monitoring real-time and batch ML models

Work closely with ML Scientists to productionise models and improve reliability, latency, and observability

Partner with backend and product teams across Depop to define integration requirements and coordinate deployments of shared ML components

Help design and extend the ML platform at Depop in collaboration with the MLOps team, across areas such as:

Robust prototyping and training workflows

CI/CD for model deployment

Real-time and batch model serving

Online/offline feature consistency via our feature store

Monitoring and alerting

Hold high standards for operational excellence, including testing, monitoring, maintainability, and incident response

Contribute to a strong ML engineering culture focused on scalability, collaboration, and continuous learning

Required Skills and Experience

Proven track record of building and deploying ML pipelines and contributing to ML platform tooling

Solid understanding of ML workflows and experience supporting scientists through to production

Strong ownership mindset and ability to work independently

Excellent communication skills across technical and non-technical stakeholders

Experience designing systems in modern cloud environments (e.g. AWS, GCP)

Technologies and Tools

Python

ML and MLOps tooling (e.g. SageMaker, Databricks, TFServing, MLflow)

Common ML libraries (e.g. scikit-learn, PyTorch, TensorFlow)

Spark and Databricks

AWS services (e.g. IAM, S3, Redis, ECS)

Shell scripting and related developer tooling

CI/CD tools and best practices

Streaming and batch data systems (e.g. Kafka, Airflow, RabbitMQ)