Machine Learning Engineer

Rivet Dating; Gurugram District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Rivet Dating is building a dating network focused on meaningful connections through human-centered matching. You’ll join a founding team to design and deploy core recSys powering our matchmaking experience.

You will own the full lifecycle of building and deploying ranking systems, embeddings, and real-time models, collaborating with data, product and backend teams to deliver delightful user experiences at scale.

Qualifications

• 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 popular recommendation and personalisation techniques: collaborative filtering, deep retrieval models, learning-to-rank, embeddings with ANN search, and LLM approaches for sparse data personalisation.
• Experience with training models OR deploying them – 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.

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 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

Skills

RecSys design

B2C product

Recommendation techniques

End-to-end ML pipelines

Evaluation & A/B testing

Vector search/Graph algorithms

LLM for sparse data

Job description

is 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.

is 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.

Rivet Dating

is 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 Role

You’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 great customer experiences
• Deploy models to production using fast iteration loops, model registries, and observability tooling

Ideal Profile

You 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