DevOps Engineer | SDE - 1 | SDE  - 2

Role: Machine Learning Engineer – Recommendations & Personalization

Function: Machine Learning / AI Engineering

Location: Gurugram, India (Sector 29, IFFCO Tower)

Type: Full-time

Compensation: Competitive cash compensation benchmarked to top-tier startups in India; significant ESOPs with real wealth creation potential

Industry: Internet / Consumer Tech / Dating & Social

About Company

A venture-backed, stealth-stage startup reimagining human connection through intelligence — not swipes. The company has raised $10M+ in seed funding from marquee VCs in India and the US.

It is building a real-time ML recommendation engine that delivers deeply personalized matchmaking, even in sparse data scenarios. Think Spotify for moods or TikTok for discovery, but for human connection.

The company's target market is individuals seeking deeper, more meaningful relationships. With a 23-person team based in Gurugram, the founders bring extensive experience scaling dating apps and global consumer internet businesses.

Position Overview

We're looking for a Senior ML Engineer to own the company's real-time adaptive matchmaking and personalization engine — the core of the product experience. You'll design and ship recommendation, ranking, and retrieval systems that work in sparse data environments and deliver deeply curated experiences at scale. This is a high-autonomy, high-impact role for someone who can go end-to-end — from modeling to production.

Role & Responsibilities

• Own and evolve the matchmaking, recommendation, ranking, and personalization systems end-to-end
• Build a real-time adaptive matchmaking engine that learns continuously from user interaction signals
• Design and implement user embedding systems, similarity models, and graph-based match scoring frameworks
• Develop ranking and retrieval algorithms (two-tower, learning-to-rank, ANN search) to make each user's feed feel curated
• Explore and integrate cold-start solutions for new users with sparse behavioral data
• Deploy models to production using fast iteration loops, model registries, and observability tooling
• Partner with Data, Product, and Backend teams to translate user signals into measurable improvements in match quality

Must Have Criteria

• 3–7 years of experience working on personalization, recommendations, search, or feed ranking in production
• Prior experience at a B2C product company — social, e-commerce, gaming, fashion, or video platforms
• Hands-on experience with collaborative filtering, deep retrieval models (two-tower), and learning-to-rank techniques
• Experience building or deploying end-to-end ML pipelines — from feature engineering to model serving
• Strong proficiency in Python with ML frameworks (PyTorch preferred) and vector/ANN search libraries (e.g., FAISS, Pinecone)
• Solid understanding of offline and online evaluation, A/B testing design, and metric alignment for recommendation systems
• Experience with cold-start problem-solving strategies in sparse data or low-signal user environments

Nice to Have

• Experience with graph-based recommendation algorithms (e.g., GraphSAGE, GNNs) applied to user-item matching
• Familiarity with LLM-based approaches for personalization — using language signals for sparse data scenarios
• Prior experience as an early or founding ML hire at a seed or Series A consumer tech startup
• Exposure to real-time feature computation using streaming platforms like Kafka or Flink
• Experience with ML observability tooling and model drift detection in production systems

What We Offer

• Significant ESOPs with real wealth creation potential as a founding-team member
• Competitive cash compensation benchmarked to top-tier startups in India
• Full ownership of core ML systems with high autonomy and a fast iteration culture
• Work directly with hands-on founders on problems at the frontier of AI and human connection
• Be part of shaping the future of how humans connect in the AI era