Senior Machine Learning Engineer, Recommendations

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

Depop is looking for a Machine Learning Engineer to join the Recommendations 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 product recommendations across key surfaces across the app.

You will:

• Design and implement pipelines for training, evaluating, deploying, and monitoring retrieval models

• Work closely with ML Scientists to productionise recommendation models, improving reliability, latency, and observability

• Build and optimise embedding generation and recommendations serving

• Partner with backend and product teams to define integration requirements and coordinate deployments of recommendation services

• Help extend the recommendations ML infrastructure in collaboration with MLOps, including:

  • Reproducible training workflows

  • CI/CD for model deployment

  • Real-time and batch model serving

  • Online/offline feature consistency

  • Monitoring and alerting
     

• Maintain high standards for operational excellence, testing, and incident response

• Contribute to a strong engineering culture focused on scalability, experimentation, and measurable impact
   

• Proven experience building and deploying ML pipelines in production

• Experience with recommendation, retrieval, or ranking systems (e.g. two-tower models, embeddings, candidate generation)

• Solid understanding of ML workflows from research 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)
   

• Python

• ML 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 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, 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!