Machine Learning Engineer

🧭 About Us – MeantToBe Inc.

next-generation matchmaking and relationship platforms

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

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The Role

As the founding ML lead, you’ll design and deploy the core recommendation and personalisation systems that power our matchmaking experience. You’ll own the full lifecycle - 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
• Build the ML engineering team and culture

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

• 5+ 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.
• Can

  train models AND ship them

  – experience with end-to-end ML pipelines 
• Understands 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