ML Engineer

ML EngineerLocation: GurgaonTerm: Full-timeApplyApply for this jobJob descriptionWe are a venture-backed, stealth-stage technology company building next-generation matchmaking and relationship platforms. Our mission is to reimagine how people connect, using AI, community, and content as our building blocks.We're not building another dating app — we're creating an experience where users feel: "This app gets me."At the core of our product is a real-time, ML recommendation engine — similar to Spotify for song moods or TikTok for discovery.Most dating apps struggle to recommend the right match because they simply don't know enough about you. When they try to gather more data, they face a tradeoff - fewer users stick around, and the pool shrinks. Swipe fatigue is real. We've found a novel way to break this loop to deliver deeply personalised recommendations - even in sparse data scenarios.We are well funded and backed by marquee VCs in India and the US.The RoleYou'll design and deploy as part of a team the core recommendation and personalisation systems that power our matchmaking experience. You'll own the full lifecycle of building recSys - from design to deployment - while laying the foundation for scalable, real-time ranking infrastructure.

What You'll DoOwn and develop match-making, recommendation, ranking and personalisation systemsWork on creating a novel real-time adaptive matchmaking engine that learns from user interactions and other signalsDesign ranking and recommendation algorithms that make each user's feed feel curated for themBuild user embedding systems, similarity models, and graph-based match scoring frameworksExplore and integrate cold-start solutionsPartner with Data + Product + Backend teams to deliver great customer experiencesDeploy models to production using fast iteration loops, model registries, and observability toolingIdeal ProfileYou are a full-stack ML data scientist-engineer who can design, model, and deploy recommendation systems and ideally have led initiatives in recsys, feed ranking, or search.2-7 years of experience working on personalisation, recommendations, search, or ranking at scalePrior experience in a B2C product - social, ecommerce, fashion, dating, gaming, or video platformsExposure 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 personalisationExposure to training models OR deploying them - experience with end-to-end ML pipelinesUnderstand offline and online evaluation, A/B testing, and metric alignmentExperience with vector search, graph-based algorithms and LLM-based approaches is a big plusWhy Join Us NowJoin a founding team where your work is core to the product experienceShape the future of how humans connect in the AI eraSignificant ESOPs and wealth creation + competitive cash compensation