Senior Machine Learning Engineer

**Role:** Senior ML Engineer – Recommendations & Personalisation **Function:** Machine Learning / Data Science **Location:** Gurugram **Type:** Full-time **Industry:** Internet / Consumer Tech / Dating & Relationships **About Company** 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. Backed by marquee VCs in India and the US, the company has raised $10M+ at seed stage. The team of 23 operates with the urgency and autonomy of a founding-stage environment. Founders bring extensive experience launching and scaling dating apps and global consumer internet businesses. **Position Overview** 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. **Role & 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 **Must Have Criteria** * 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 **What We Offer** * 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