Overview

Function: Machine Learning / Data Science

Responsibilities

• Design, build, and own end-to-end matchmaking, recommendation, ranking, and personalisation systems
• Develop a real-time adaptive matchmaking engine that learns continuously from user interactions and behavioural signals
• Build user embedding systems, similarity models, and graph-based match scoring frameworks
• Architect and implement cold-start solutions to deliver quality recommendations for new users with sparse data
• Deploy models to production with fast iteration loops, model registries, and observability tooling
• Define and track offline and online evaluation frameworks, A/B tests, and metric alignment across the recommendation stack
• Partner with Data, Product, and Backend teams to translate business goals into scalable ML solutions
• 2–5 years of hands-on experience building personalisation, recommendation, ranking, or search systems at scale
• Prior experience at a B2C product company or startup — social, e-commerce, fashion, gaming, dating, or video platforms
• Working knowledge of collaborative filtering, two-tower deep retrieval models, learning-to-rank, and embedding-based ANN search
• End-to-end ML pipeline ownership — both training models and deploying them to production
• Familiarity with offline and online evaluation methodologies, A/B testing, and metric alignment

Nice to have

• Experience with vector search, graph-based algorithms, or LLM-based approaches for sparse data personalisation
• Prior ownership or leadership of a recsys, feed ranking, or search initiative
• Exposure to cold-start problem-solving in low-data or new-user scenarios
• Background at a high-scale B2C product or consumer startup

Benefits

• Direct ownership of systems that are core to the product — not peripheral tooling
• Autonomy to shape ML architecture and infrastructure from the ground up
• Collaborative, experimentation-driven culture with responsive, growth-oriented founders