Rivet Datingis the world’s first dating network, designed to help people find love and meaningful connections together rather than searching alone. Built on Social Matching, Rivet leverages the collective intuition of a diverse community to surface introductions that conventional swipe-based apps might miss. Users can join either to seek their own connections or to act as Matchers who recommend potential pairs, making the experience collaborative and human-centered.

By prioritizing real human judgment over purely algorithmic matching, Rivet aims to make dating social, inclusive, and authentic. Rivet is currently available across the United States.

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 Do

- 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 outstanding customer experiences
- Deploy models to production using fast iteration loops, model registries, and observability tooling

Ideal 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 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.
- Exposure to training models OR deploying 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

Why Join Us Now

- Join a founding team where your work is core to the product experience
- Shape the future of how humans connect in the AI era
- Significant ESOPs and wealth creation + competitive cash compensation

📌 Machine Learning Engineer (Gurugram)
🏢 Rivet Dating
📍 Gurugram