Senior Machine Learning Engineer, Ranking

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

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)

Additional information

• Health + Mental Wellbeing: PMI and cash plan healthcare access with Bupa, subsidised counselling and coaching with Self Space.
• Cycle to Work scheme with options from Evans or the Green Commute Initiative.
• Employee Assistance Programme (EAP) for 24/7 confidential support.
• Mental Health First Aiders across the business for support and signposting.
• Work/Life Balance: 25 days annual leave with option to carry over up to 5 days, 1 company‑wide day off per quarter, impact hours: Up to 2 days additional paid leave per year for volunteering, fully paid 4 week sabbatical after completion of 5 years of consecutive service with Depop.
• Flexible Working: MyMode hybrid‑working model with Flex, Office Based, and Remote options (role dependant). All offices are dog‑friendly. Ability to work abroad for 4 weeks per year in UK tax treaty countries.
• Family Life: 18 weeks of paid parental leave for full‑time regular employees, IVF leave, shared parental leave, and paid emergency parent/carer leave.
• Learn + Grow: Budgets for conferences, learning subscriptions, and more, mentorship and programmes to upskill employees.
• Your Future: Life Insurance (financial compensation of 3x your salary), pension matching up to 6% of qualifying earnings.
• Depop Extras: Employees enjoy free shipping on their Depop sales within the UK. Special milestones are celebrated with gifts and rewards!