Role: Senior ML Engineer – Recommendations & Personalisation Function: Machine Learning / Data Science
Type: Full time
Internet / Consumer Tech / Dating & Relationships

A venture-backed, stealth-stage startup reimagining how people connect. The company is building a next-generation matchmaking platform where AI, community, and content come together.
At its core is a real-time ML recommendation engine delivering deeply personalised experiences — even in sparse data scenarios. Founders bring extensive experience launching and scaling dating apps and global consumer internet businesses.

This role owns the full lifecycle of the company's matchmaking and personalisation engine — from architecture through production deployment. You will build the real-time ranking and recommendation infrastructure that makes every user's experience feel individually curated, partnering closely with Data, Product, and Backend teams to ship systems that directly define the product.

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




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

Experience with vector search, graph-based algorithms, or LLM-based approaches for sparse data personalisation
Exposure to cold-start problem-solving in low-data or new-user scenarios
Autonomy to shape ML architecture and infrastructure from the ground up

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