Senior Machine Learning Engineer, Ranking

Depot
Charing Cross, United Kingdom
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Remote
Charing Cross, United Kingdom

Tech stack

Airflow
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Continuous Integration
Data Systems
Identity and Access Management
Python
Machine Learning
RabbitMQ
Redis
TensorFlow
Data Streaming
PyTorch
Spark
Backend
Scikit Learn
Kafka
Operational Systems
Machine Learning Operations
Databricks

Job description

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 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.

Requirements

  • 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)

Benefits & conditions

Pulled from the full job description

  • Sabbatical
  • Life insurance
  • Employee assistance programme
  • Additional leave
  • Company pension
  • Paid volunteer time
  • Cycle to work scheme, * 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, 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 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!

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