Senior Machine Learning Engineer, Recommendations
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Job Summary
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 out more at
Our mission is to make fashion circular and to create an inclusive environment where everyone is welcome no matter who they are or where theyre 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. Were 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. Were 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 . 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!
Required Experience:
Senior IC
Key Skills
- Industrial Maintenance
- Machining
- Mechanical Knowledge
- CNC
- Precision Measuring Instruments
- Schematics
- Maintenance
- Hydraulics
- Plastics Injection Molding
- Programmable Logic Controllers
- Manufacturing
- Troubleshooting
