ML Engineer

You own the full lifecycle of the recommendation system, from design through production.

• The matchmaking recommendation engine. Build and iterate the real-time adaptive system that learns from user and community interactions.
• Ranking and personalization algorithms. Design the ranking logic that makes each user's feed feel individually curated.
• User embedding and similarity frameworks. Build embedding systems, similarity models, and graph-based match scoring pipelines.
• Cold-start solutions. Explore and integrate approaches that deliver quality recommendations even in sparse data conditions.
• Production ML infrastructure. Deploy models using fast iteration loops, model registries, and observability tooling.
  • Cross-functional delivery. Partner with Data, Product, and Backend to ship experiences that work end to end.