Senior Machine Learning Engineer, Recommendations

Company DescriptionDepop 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 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 . For any other non-disability related questions, please reach out to our Talent Partners.RoleDepop 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.ResponsibilitiesYou will:Design and implement pipelines for training, evaluating, deploying, and monitoring retrieval modelsWork closely with ML Scientists to productionise recommendation models, improving reliability, latency, and observabilityBuild and optimise embedding generation and recommendations servingPartner with backend and product teams to define integration requirements and coordinate deployments of recommendation servicesHelp extend the recommendations ML infrastructure in collaboration with MLOps, including:Reproducible training workflowsCI/CD for model deploymentReal-time and batch model servingOnline/offline feature consistencyMonitoring and alertingMaintain high standards for operational excellence, testing, and incident responseContribute to a strong engineering culture focused on scalability, experimentation, and measurable impactRequired Skills and ExperienceProven experience building and deploying ML pipelines in productionExperience with recommendation, retrieval, or ranking systems (e.g. two-tower models, embeddings, candidate generation)Solid understanding of ML workflows from research to productionStrong ownership mindset and ability to work independentlyExcellent communication skills across technical and non-technical stakeholdersExperience designing systems in modern cloud environments (e.g. AWS, GCP)Technologies and ToolsPythonML frameworks (e.g. PyTorch, TensorFlow, scikit-learn)ML/MLOps tooling (e.g. SageMaker, MLflow, TFServing)Spark and DatabricksAWS services (e.g. IAM, S3, Redis, ECS)CI/CD tooling and best practicesStreaming and batch systems (e.g. Kafka, Airflow, RabbitMQ)Additional InformationHealth + Mental WellbeingPMI and cash plan healthcare access with BupaSubsidised counselling and coaching with Self SpaceCycle to Work scheme with options from Evans or the Green Commute InitiativeEmployee Assistance Programme (EAP) for 24/7 confidential supportMental Health First Aiders across the business for support and signpostingWork/Life Balance:25 days annual leave with option to carry over up to 5 days1 company-wide day off per quarterImpact hours: Up to 2 days additional paid leave per year for volunteeringFully 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 dependantAll offices are dog-friendlyAbility to work abroad for 4 weeks per year in UK tax treaty countriesFamily Life:18 weeks of paid parental leave for full-time regular employeesIVF leave, shared parental leave, and paid emergency parent/carer leaveLearn + Grow:Budgets for conferences, learning subscriptions, and moreMentorship and programmes to upskill employeesYour Future:Life Insurance (financial compensation of 3x your salary)Pension matching up to 6% of qualifying earningsDepop Extras:Employees enjoy free shipping on their Depop sales within the UK.Special milestones are celebrated with gifts and rewards!