Senior machine learning scientist

Depop
London

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 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.

Role

Depop is looking for a Senior Machine Learning Scientist to join our new Core ML team in the UK. You will work alongside a cross-functional team of Product Managers, ML Engineers, and fellow ML Scientists, helping build and maintain foundational machine learning models and infrastructure, such as product matching models, image embedding services, and lightweight classifiers, that support multiple product and marketing use cases across Depop.

As a senior member of the team, you will be expected to take ownership of high-impact projects, lead technical direction on core modelling efforts, and mentor others while working across multiple domains and stakeholders.

Responsibilities

You will:

  • Research, design, and deliver robust machine learning solutions to solve cross-cutting problems within the fashion resale space
  • Work with and fine-tune models for representation learning, computer vision, and classification, and lead efforts to productionise and scale them
  • Identify and define requirements from multiple stakeholders across the business, and lead the design of general-purpose machine learning solutions that power features like content understanding, moderation, and personalization
  • Set up and conduct large-scale experiments to test hypotheses and guide model and product improvements, ensuring statistical rigour and real-world applicability
  • Stay up to date with research, actively contribute to internal knowledge sharing and ML best practices, and help shape the long-term technical strategy for the team
  • Participate in team ceremonies, such as agile cadences, technical whiteboarding sessions, and planning/roadmapping
  • Communicate technical findings clearly and confidently to both technical and non-technical audiences, including senior stakeholders


Qualifications

Skills and Experience:

  • Significant experience working as a Machine Learning Scientist, with a proven track record of delivering and scaling models that solve complex, real-world problems
  • Deep understanding of machine learning concepts and experience applying them in production settings, using frameworks such as Transformers, PyTorch, or TensorFlow
  • Strong Python skills, with the ability to write clean, modular, production-grade code, and a solid understanding of data engineering and MLOps principles
  • Ability to lead end-to-end ML projects, work independently in ambiguous problem spaces, and mentor junior team members
  • Strong collaboration and communication skills, with experience aligning technical approaches with cross-functional teams and stakeholders


Bonus Points

  • Experience with NLP, image classifiers, deep learning, or large language models
  • Experience with experiment design and conducting A/B tests
  • Experience building shared or platform-style ML systems
  • Experience with Databricks and PySpark
  • Experience working with AWS or another cloud platform (GCP/Azure)


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!
Posted 2025-09-10

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