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As a Senior Data Platform Engineer at Depop, you'll architect and build data platforms, working with various teams to enhance data capabilities, support complex business problems, and promote self-service tools while ensuring operational excellence and effective stakeholder collaboration.

Lead design, deployment, and iteration of large-scale ranking models (recommendations and search). Collaborate cross-functionally, manage end-to-end ML lifecycle, drive research and innovation, and mentor team members while delivering measurable product outcomes.

The Finance Integration Lead will manage finance systems integration, lead process design, ensure communication, and manage risks while supporting the transition to new ERP systems.

Lead and develop a cross-functional backend and iOS squad focused on trust, safety, and compliance. Own delivery, team health, and product outcomes while balancing user safety, fairness, usability, legal obligations, and technical quality. Drive iteration, experimentation, and measurable impact, engage on architecture and technical trade-offs, unblock decisions, and build an inclusive team culture.

As an Associate Backend Software Engineer, you will collaborate with teams to design solutions, produce high-quality code for a user-impacting product, and engage in continuous improvement practices.

The Staff Machine Learning Scientist will design, develop, and maintain machine learning models for Depop's fashion resale platform, leading technical initiatives and collaborating across teams to drive innovation in ML solutions.

Join the Platform Core team to design, operate, and automate Depop's AWS-based infrastructure. Build and maintain Terraform/IaC, CI/CD pipelines, Cloudflare edge configurations, and platform tooling using Python/Golang. Mentor engineers, participate on-call, improve security posture, and drive platform reliability and developer self-service.

Senior Backend Software Engineer to own and evolve Depop's core backend systems. Improve scalability, reliability, observability and operational safety; design and implement backend services; collaborate across engineering, product, security and data; support production incidents; influence architecture; mentor colleagues; and drive platform capabilities used by many users.

Lead the design and deployment of recommendation systems, mentor team members, collaborate across functions, and innovate using emerging ML techniques.

Lead analytics and technical discovery for a core product area, partner with product/engineering/ML teams, design and evaluate A/B experiments, build scalable datasets and dashboards, and influence product and commercial strategy through rigorous analysis and ML evaluation support.