About the role

You will build the algorithms that connect local consumers with the right businesses at the right moment — search ranking, two-sided marketplace matching, and dispatch optimization. This is the engine of a local marketplace: learning-to-rank and retrieval pipelines, matching and dispatch models, and the relevance and recommendation systems that move marketplace conversion. If you have owned ranking or matching models that improved conversion at a marketplace, this role is the heart of the product.

What you'll do

• Build search-ranking, marketplace-matching, and dispatch models for a two-sided local marketplace.
• Own learning-to-rank and retrieval/recall pipelines end to end.
• Improve marketplace conversion through relevance, matching, and recommendation models.
• Design and analyze experiments (A/B, switchback) to prove causal impact in a marketplace setting.
• Bring modern ML/LLM techniques into ranking and matching where they add real lift.

What we're looking for

• 4+ years as an ML Engineer / Applied Scientist on ranking, matching, dispatch, retrieval, or recommendation.
• Direct work on two-sided marketplace matching or dispatch, or on search ranking with a learning-to-rank / retrieval pipeline.
• Demonstrated marketplace-conversion impact (you can point to the lift and how you measured it).
• Strong experimentation skills, including causal methods for marketplaces (e.g., switchback).
• Production ML at scale.

Nice to have

• Background at DoorDash, Uber, Instacart, Airbnb, Yelp, Nextdoor, Lyft, Amazon, or Faire.
• Relevance / matching work that connects consumers with local businesses (Yelp, Nextdoor).
• Didi / Meituan dispatch-and-matching experience, now US-based.