{"id":2120423,"url":"https://alion.io/job/depop-senior-machine-learning-engineer-ranking","title":"Senior Machine Learning Engineer, Ranking","company":{"id":51473,"name":"Depop","domain":"depop.com","url":"https://alion.io/company/depop","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":["GB"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":95000,"max_usd":175000,"period":"year","method":"role_seniority_country_cell","sample_n":39},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"IAM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RabbitMQ","optional":false},{"name":"Redis","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-09-16T00:00:00Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-10-11T17:48:20Z","board_verified":true,"closed_at":null,"days_open":25,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":25},"description":"Company Description\nDepop is a peer-to-peer circular fashion marketplace where anyone can buy, sell and discover secondhand fashion. Our mission is simple: to make fashion circular by making secondhand as exciting and rewarding as buying new.\nFounded in 2011, Depop’s diverse community has helped move resale into the mainstream, where buying secondhand is no longer an alternative, but how people of different ages now engage with fashion. Today, more than 56 million registered users come to Depop to find great value, express their own personal style and give clothes a longer life. We believe that everything you want already exists, and our role is to help people discover it.\nPowered by a team of over 500 people, our company is headquartered in London, with offices in New York. In 2021, Depop became a wholly-owned subsidiary of Etsy - the global marketplace for unique and creative goods - and continues to operate as a standalone company. For more information, visit www.depop.com\nWe aim 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.\nWe’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.\nAI Disclosure: We use AI tools (Google Gemini) to help our team source and review applications for roles with a high volume of applications. These tools assist our recruiters in identifying great talent but do not replace human decision-making. At Depop, every hiring decision is made by a human.\nIf, 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 .\nRole\nDepop 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.\nThe 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.\nAs 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.\nResponsibilities\nYou will:\nDesign and implement pipelines for training, evaluating, deploying, and monitoring learning-to-rank models.\n\nWork closely with ML Scientists to productionise ranking models, improving reliability, latency, and observability.\n\nBuild and optimise real-time model serving systems that deliver personalised rankings across the app.\n\nPartner with backend and product teams to define integration requirements and coordinate deployment of ranking services.\n\nHelp extend the ML infrastructure for ranking systems in collaboration with the MLOps team, including:\nReproducible model training workflows\n\nCI/CD pipelines for model deployment\n\nReal-time and batch model serving\n\nOnline/offline feature consistency through the feature store\n\nMonitoring and alerting for production models\n\nMaintain high standards for operational excellence, including testing, monitoring, maintenance, and incident response.\n\nContribute to a strong engineering culture focused on scalability, experimentation, and measurable impact.\n\nRequired Skills and Experience\nProven experience building and deploying machine learning pipelines in production environments.\n\nExperience working with ranking, recommendation, or retrieval systems.\n\nStrong understanding of machine learning workflows, from experimentation to production deployment.\n\nExperience designing and operating systems in modern cloud environments (e.g. AWS or GCP).\n\nStrong ownership mindset with the ability to work independently in a fast-moving environment.\n\nExcellent communication skills and the ability to collaborate with cross-functional stakeholders.\n\nA genuine curiosity about AI and a willingness to explore how it can enhance your day-to-day work\n\nTechnologies and Tools\nPython\n\nMachine learning frameworks (e.g. PyTorch, TensorFlow, scikit-learn)\n\nML / MLOps tooling (e.g. SageMaker, MLflow, TFServing)\n\nSpark and Databricks\n\nAWS services (e.g. IAM, S3, Redis, ECS)\n\nCI/CD tooling and best practices\n\nStreaming and batch data systems (e.g. Kafka, Airflow, RabbitMQ)\n\nAdditional Information\nHealth + Mental Wellbeing\nPMI and cash plan healthcare access with Bupa\nSubsidised counselling and coaching with Self Space\nCycle to Work scheme with options from Evans or the Green Commute Initiative\nEmployee Assistance Programme (EAP) for 24/7 confidential support\nMental Health First Aiders across the business for support and signposting\n\nWork/Life Balance:\n25 days of annual leave with the option to carry over up to 5 days\nImpact hours: Up to 2 days of additional paid leave per year for volunteering\nFully 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.\nFlexible Working: MyMode hybrid-working model with Flex, Office-Based, and Remote options *role-dependent\nAll offices are dog-friendly\nFamily Life:\nFor birth parent: 20 weeks of paid parental leave for full-time regular employees\nFor non-birth parents: 12 weeks of paid parental leave for full-time regular employees \nIVF leave, shared parental leave, and paid emergency parent/carer leave\nLearn + Grow:\nTwice-yearly development chats and yearly performance reviews\nLearning budget\nUpskilling our employees with company-wide training workshops, materials and resources\nYour Future:\nLife Insurance (financial compensation of 3x your salary)\nPension matching up to 6% of full base salary with Aviva\nDepop Extras:\nIn-office Depop Shop (that’s free!) and a packing station with free delivery.\nSpecial milestones are celebrated with gifts and rewards!","description_format":"text","description_chars":6675,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Annual leave","Flexible schedule","Life insurance","Parental leave"],"hiring_locations":[{"name":"United Kingdom","iso":"GB","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Marketplaces","Secondhand"],"lifecycle":[{"event":"open","at":"2026-10-08T23:57:16Z"}],"visa":[{"country":"GB","licensed_sponsor":true,"evidence":"Licensed UK visa sponsor (Senior or Specialist Worker, Skilled Worker)","filings_12m":null,"filings_prev_12m":null,"green_card_filings_12m":null,"median_offered_wage_usd":null,"route":"Global Business Mobility: Senior or Specialist Worker; 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