Senior Machine Learning Engineer, Ranking

Role

Responsibilities

• 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.

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.
• A genuine curiosity about AI and a willingness to explore how it can enhance your day-to-day work
• 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
• 25 days of annual leave with the option to carry over up to 5 days
• Impact hours: Up to 2 days of additional paid leave per year for volunteering
• Fully paid 4-week sabbatical after completion of 5 years of consecutive service with Depop, to give you a chance to recharge or do something you love.
• Flexible Working: MyMode hybrid-working model with Flex, Office-Based, and Remote options role-dependent
• All offices are dog-friendly
• For birth parent: 20 weeks of paid parental leave for full-time regular employees
• For non-birth parents: 12 weeks of paid parental leave for full-time regular employees
• IVF leave, shared parental leave, and paid emergency parent/carer leave
• Twice-yearly development chats and yearly performance reviews
• Learning budget
• Upskilling our employees with company-wide training workshops, materials and resources
• Life Insurance (financial compensation of 3x your salary)
• Pension matching up to 6% of full base salary with Aviva
• In-office Depop Shop (that’s free!) and a packing station with free delivery.
• Special milestones are celebrated with gifts and rewards!

Technologies and Tools