This role is for one of our partner companies, Consumer tech, seed raised $10M

About the role:

We are looking for an ML Engineer to build and own the recommendation and personalization systems at the heart of the product.

You will work on the matchmaking engine, ranking algorithms, user embeddings, similarity models, and cold-start problems that determine who gets shown to whom.

This is a hands-on IC role for someone who enjoys taking ambiguous consumer-product problems, translating them into ML problems, finding signal in noisy behavioral data, and shipping systems into production.

You will work closely with the Principal ML Engineer and partner with Product, Data, and Backend teams.

What you'll own:
Build and iterate on the core matchmaking recommendation engine
Develop ranking and personalization algorithms for individually curated feeds
Build user embeddings, similarity models, and graph-based matching systems
Solve cold-start and sparse-data problems
Work with behavioral and community interaction data to identify useful recommendation signals
Design offline and online evaluation frameworks for recommendation quality
Deploy models into production and improve iteration,



monitoring, and observability
Partner closely with Product, Data, and Backend teams to ship end-to-end experiences
Identify recent ML opportunities rather than simply execute predefined tasks

You'll be a excellent fit if:
You have built recommendation or personalization systems in a live product
You have worked on feed ranking, search ranking, discovery, matching, or recommendations
You can translate ambiguous product questions into clearly defined ML problems
You are comfortable working with noisy, sparse, or incomplete behavioral data
You have taken ML models from experimentation into production
You understand recommendation evaluation, experimentation, and A/B testing
You have experience with one or more of:
Cooperative filtering
Two-tower retrieval
Learning-to-rank
Embeddings and ANN search
Similarity models
Graph-based recommendations
LLM-based personalization

Experience with consumer products such as social, e-commerce, video, marketplaces, dating, jobs, or content discovery is particularly relevant.

📌 Ml Engineer 2 5 Years, 50l Bengaluru (India)
🏢 profound.me
📍 India