Senior Machine Learning Engineer, Ranking

Machine Learning Engineer

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.

Responsibilities

• 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.

Required Skills and Experience

• 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.

Technologies and Tools

• 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)