ML Engineer

Responsibilities:

• Own and develop match-making, recommendation, ranking, and personalisation systems.
• Work on creating a novel, real-time adaptive matchmaking engine that learns from user interactions and other signals.
• Design ranking and recommendation algorithms that make each user's feed feel curated for them.
• Build user embedding systems, similarity models, and graph-based match scoring frameworks.
• Explore and integrate cold-start solutions.
• Partner with Data + Product + Backend teams to deliver great customer experiences.
• Deploy models to production using fast iteration loops, model registries, and observability tooling.

Requirements:

• 3-5 years of experience working on personalisation, recommendations, search, or ranking at scale.
• Prior experience in a B2C product - social, ecommerce, fashion, dating, gaming, or video platforms.
• Exposure to a wide range of popular recommendation and personalisation techniques, including collaborative filtering, deep retrieval models (e. g., two-tower), learning-to-rank, embeddings with ANN search, and LLM approaches for sparse data personalisation.
• Can train models AND ship them - experience with end-to-end ML pipelines.
• Understand offline and online evaluation, A/B testing, and metric alignment.
• Experience with vector search, graph-based algorithms, and LLM-based approaches is a big plus.

Skills

ANN, ML, collaborative filtering, embeddings, learning to rank, machine learning, personalisation, personalization, ranking, recommendation, recommendations, vector search