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 adjustments@depop.com. For any other non-disability related questions, please reach out to our Talent Partners.

Role

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.

Responsibilities

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

Required Skills And Experience

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)

Technologies and Tools

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!