• Explore and apply advanced machine learning techniques, including large language models (LLMs) and deep learning, to solve complex challenges across the organization. • Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans. • Drive project execution by managing priorities, deadlines, and deliverables, leveraging your technical expertise. • Develop high-quality, well-tested Python code to address critical product and business needs. • Design scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices. • Mentor team members on design principles, coding standards, and the adoption of AI productivity tools. • Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency. • Ensure the security, scalability, and architectural integrity of feature designs through thorough reviews across teams.

• 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.

• Design, develop, test, and maintain our complex software applications, ensuring high-quality code and adherence to best practices • Provide technical leadership and guidance to junior engineers and the development team, including code reviews, architecture decisions, and mentoring • Contribute to the design and architecture of software systems, ensuring scalability, maintainability, and performance • Analyze and solve complex technical problems, and make recommendations for improvements and optimizations • Implement and advocate for best practices in testing and quality assurance, including unit testing, integration testing, and automated testing • Participate in code reviews and provide constructive feedback to ensure code quality and consistency • Stay current with emerging technologies, tools, and programming languages and apply them where relevant to improve software development processes • Ensure software adheres to security standards and compliance requirements, addressing vulnerabilities and potential risks • Design and implement cloud solutions, build MLOps on cloud (AWS, Azure, or GCP) • Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools • Run code and refactor, optimize, containerize, deploy, version, and monitor Data Science models quality • Validate and add automated tests for Data Science models • Work closely with a team of researchers and data scientists to productionize and document research innovations