Staff Machine Learning Scientist (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 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 . For any other non-disability related questions, please reach out to our Talent Partners. The Recommendations team builds models that power discovery at Depop, helping millions of users find items that they will love. As a Staff Machine Learning Scientist, you’ll set the technical vision for our next-generation recommendation models, lead high-impact initiatives, and mentor others to drive innovation at scale. Responsibilities You will: Lead the design and deployment of advanced recommendation systems, encompassing encoder-based architectures, vector representations and large-scale retrieval. Mentor, coach, and set technical direction within the Recommendations team, helping others grow and innovate. Collaborate closely with cross-functional partners (product, engineering, data) to define problems, translate them into scalable solutions, and deliver measurable business outcomes. Lead the end-to-end lifecycle of ML projects: from ideation, data acquisition, feature engineering, training, and evaluation to deployment and ongoing iteration. Drive innovation in recommendation systems by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy. Act as a thought leader in the recommendations space, sharing learnings internally, engaging with the wider ML community, and showcasing our work externally. Qualifications Proven track record in designing, deploying, and optimizing large-scale recommendation systems, including candidate retrieval and ranking models, with measurable impact in production environments. Deep understanding of machine learning fundamentals and applied experience with architectures including collaborative filtering, deep learning, and hybrid recommendation approaches. Proven ability to productionize ML models and pipelines: from prototyping to de ployment, with strong experience in monitoring, iteration, and troubleshooting. Advanced programming skills in Python and familiarity with ML frameworks such as PyTorch, TensorFlow, or similar. Solid foundation in stats, experimental design, and working with offline/online evaluations in real-world settings. Experience leading projects and mentoring engineers or scientists, with a track record of fostering team growth and technical excellence. Excellent communication skills: able to bridge technical and non-technical stakeholders and influence decision making. Committed to responsible AI practices, including attention to ethics, fairness, and inclusivity. 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!