About the Role
This position is in the India Maps team. Maps plays a foundational role in Uber; many of the core Uber product functions are built on top of or leverage Maps functionality directly or indirectly. India Maps team develops high-priority, critical pieces of functionality for the Maps organization, working on a variety of things from Routing observability, diagnostics, algorithmic map error detection, experiment simulation platform, basemap data platform to Uber Places platform, Legoland, etc.
This position falls in the Routing vertical of the Maps organization. The Maps Routing stack in Uber is a set of technologies that produces the best route from point A to point B and estimates time (ETAs) for all Uber. This includes modeling traffic on roads from GPS locations, sophisticated routing algorithms with a variety of cost functions, turn-by-turn navigation experience, and machine-learned ETAs predictions. This also requires building big data pipelines -- both batch and real-time -- to process and ingest map data, computing ETAs, traffic, etc. Our routing solutions powers many Uber products ranging from pickup ETAs, driver-rider matching (Uber Pool and others), fare estimation, EATs, Freight, Bicycles and more to come!
This role is critical to our Maps product as a whole -- when map data does not reflect the real world accurately, all the services built on top of it are adversely affected causing degradation of customer experience and safety issues. As the real world keeps changing, any map, regardless of initial quality, will always have errors; and the ability to apply ML and algorithms to detect and fix map errors is one of the most scalable ways for us to keep the maps accurate.
What You'll Do
For this job position, we are looking for a strong ML engineer who can solve challenges in Maps domain by applying ML techniques. The particular problem that this engineer will work on is to automatically detect many kinds of errors in our global map data by leveraging the huge amount of signals that is available at Uber (ex: terabytes of GPS traces, re-routes, traffic patterns, customer tickets, satellite/streetside imagery, etc). This problem is made even more challenging given that we need to detect these errors with high precision (to minimize costs of manual review) and high recall (to detect most of the significant errors present in the map).
A successful Maps ML engineer would have
Bonus points for Geospatial experience: Familiarity with geospatial datasets and services, such as maps, local search, points of interest and business listings data, mobile device location and GPS traces would be a plus but is not required.
Why join us
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.
At Uber we don’t just accept difference—we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. Uber is proud to be an equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.