Depop

• Own content end-to-end for your experience area(s) across the Depop apps (iOS & Android) and Depop.com • Partner closely with designers, researchers, product managers and engineers to ship impactful work you’re proud of • Use feedback and user testing results to iterate effectively • Elevate content quality, defining what good looks like with your teams and providing guidance to non-writers, where necessary • Work with legal and regulatory teams to make sure content you write is compliant with industry standards and regulations, where necessary • Contribute to the evolution of content standards at Depop, including writing guidelines and AI best practices

• Work closely with Product Managers, Data Scientists and other Backend Engineers to understand problems and to design solutions. • Produce high-quality code that is well-structured and simple to understand that will be used by 1M+ active daily users • Embrace agile methodologies • Engage in a culture of continuous improvement by attending events such as blameless post-mortems, architecture reviews etc. • Collaborate on a daily basis with fellow engineers in the cross functional environment to solve problems and write code • You’ll own , code, workflows and data, through their entire lifecycle • Documenting any feature development

• Independently design and deliver backend solutions to medium-to-large problem spaces, from discovery through production and iteration • Build and evolve core discovery services, pipelines and APIs that power personalisation at scale • Help improve search relevance and recommendation quality by working at the intersection of backend engineering and ML-powered personalisation • Take end-to-end ownership of your work, including design, implementation, testing, observability, and operational support • Contribute to shaping technical direction within the team, improving system design, reliability, and developer experience • Operate in an experiment-led environment, helping define hypotheses, success metrics, and feedback loops • Collaborate closely with Product, Data Science, Design, and other Engineering teams to ensure solutions are well scoped and well understood • Actively contribute to continuous improvement through retrospectives, post-incident reviews, and technical discussions • Mentor and support other engineers in the team, raising the overall quality of delivery

• Pave a path for data as a product : Champion data as a first-class citizen by introducing robust data capabilities, observability and governance into the platform to enable decision making and ultimately better experience for Depop’s end users. • Software Engineering: Develop microservices, libraries, platforms and tools • Technical Design: Architect and evolve platform services that enable teams to work with data efficiently and securely at scale. • Lead Technological Initiatives: You will lead large, complex initiatives that will further enable our data users by setting the vision and roadmap, gathering requirements and defining the scope and success metrics for your initiative all while embracing a platform as a product approach. • Uphold Operational Excellence: Automating infrastructure and monitoring, leading incident response and root cause analysis, and continuously improving the health and performance of our data platform. Champion scalable standard processes through automation, clear documentation, and knowledge sharing via tutorials and training sessions. • Stakeholder Collaboration: Work closely to establish trusted relationships with both technical and non-technical stakeholders to define, design, and implement solutions within our data platform. You will also contribute to group and peer-level initiatives to improve our data platform as a whole. • Invest in others’ growth: Mentoring and sharing knowledge, helping elevate technical standards and fostering a culture of continuous improvement. • Build on our Engineering Culture: Take an active role in improving the engineering culture at Depop and encourage others around you to follow these values.

• 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

• Design and implement pipelines for training, evaluating, deploying, and monitoring learning-to-rank models. • Work closely with ML Scientists to productionise ranking models, improving reliability, latency, and observability. • Build and optimise real-time model serving systems that deliver personalised rankings across the app. • Partner with backend and product teams to define integration requirements and coordinate deployment of ranking services. • Help extend the ML infrastructure for ranking systems in collaboration with the MLOps team, including: • Reproducible model training workflows • CI/CD pipelines for model deployment • Real-time and batch model serving • Online/offline feature consistency through the feature store • Monitoring and alerting for production models • Maintain high standards for operational excellence, including testing, monitoring, maintenance, and incident response. • Contribute to a strong engineering culture focused on scalability, experimentation, and measurable impact.

• Research, design and deliver machine learning solutions to tackle problems within the ranking space • Understand requirements from various stakeholders across the business, designing machine learning solutions to power features like real-time feed personalisation or search relevance. • Set up and conduct large-scale experiments to test hypotheses and drive product development • Keep up to date with research, contribute to internal knowledge sharing and ML best practices, and help the team stay informed about new techniques and approaches relevant to our work • Participate in team ceremonies, such as agile cadences, technical whiteboarding sessions, and planning/roadmapping • Report and present technical findings to technical and non-technical audiences

• Lead the design and deployment of advanced ranking models for Depop’s entire app, covering personalised recommendations, search results and other surfaces. • 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 ranking by researching and integrating emerging ML techniques, frameworks, and tooling, while contributing technical expertise to long-term product and data strategy. • Mentor, coach, and set technical direction within the Ranking ML team, helping others grow and innovate. • Act as a thought leader in the ranking space, sharing learnings internally, engaging with the wider ML community, and showcasing our work externally.

• Own the design, development, and deployment of 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 own efforts to productionize, scale, and evolve them as shared systems • Partner closely with senior stakeholders across the business to define problems, and lead the design of general-purpose, scalable ML solutions that power features like content understanding, moderation, and personalisation • Lead the end-to-end lifecycle of large-scale experiments, from hypothesis generation through evaluation, to guide model and product improvements, ensuring statistical difficulty and real-world applicability • Stay up to date with research, actively contribute to internal knowledge sharing and ML best practices, and chip in technical expertise to long-term product and data strategy • Participate in team ceremonies, such as agile cadences, technical whiteboarding sessions, and planning/roadmapping, setting technical direction and improving for the team • Communicate technical findings clearly and confidently to both technical and non-technical audiences, including senior stakeholders, and influence decision making