About the Role
Are you interested in working at the intersection of machine learning (ML), engineering, and data science? Are you passionate about developing next-generation algorithms to power a variety of unique solutions in the recommendation, search and knowledge graph space? If so, then this is the job for you.
We are looking for a brilliant data science lead/manager in the SF office to spearhead the challenging modeling and algorithmic development work in the Consumer (Eater) facing product area for Uber Eats.
What You'll Do
Uber Eats is Uber's ambitious and rapidly expanding on-demand food delivery business currently operating in more than 45 countries globally and is the largest outside of China. Applied Machine Learning Scientists in Uber Eats solve many exciting problems in the Recommendation, Search and Knowledge Graph space:
Recommendation: We help users discover the food they love through personalized recommendation, which jointly optimizes multiple objectives such as user engagement, restaurant demand, and long-term health of the marketplace.
Search: We help connect users with what they are looking for, let it be a cuisine, restaurant, or dish. We understand their intention and optimize their query, proactively suggest relevant search queries, build novel algorithms to power both search retrieval and ranking.
Knowledge Graph: To enhance the recommendation and search capabilities, we build an extensive knowledge graph to capture the relationship between food, restaurants, users and other marketplace entities using a wealth of data unique to Uber and Uber Eats.
At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 600 cities around the world.
We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let's move the world forward, together.