What You’ll Do
- Lead research projects spanning signal processing, sensor fusion, positioning using GPS / GNSS, WiFi, inertial sensors and high-performance engineering systems to deliver critical insight to core business flows
- Productize sensor fusion and machine learning algorithms for better location accuracy, driver/rider safety and other inferences used in Uber product
- Serve as a resource for other individuals on the team-- mentoring junior engineers and advising leaders
- Bridge industry and research, keeping the team focused on high-value problems at the cutting edge of emerging trends
- Build the team’s profile both internally and externally, attending and presenting at conferences
What You’ll Need
- Experience in the field of location and state estimation: GPS/GNSS, GPS IMU fusion.
- Experience applying state estimation (Kalman filters, extended Kalman filters, particle filters, etc.), signal processing, sensor fusion for determining position
- Excellent programming and algorithmic skills (we mainly use Java & Python)
- 10+ years experience with track record of shipping high-impact research projects
Bonus Points If
- PhD in related field (signal processing)
- Prior work in the areas of indoor location, pedestrian dead reckoning at the software layer.
- Experience working with other sensor types (audiovisual, barometric, etc.) or with mobile devices of varying quality
About the Team
Uber is deeply rooted in the physical world -- our business requires a clear understanding of complicated real-world interactions and behaviors, observed primarily through phone sensors. The accuracy of the “Blue Dot” is an important building block and influences downstream users like ETA, Traffic, Routing, Safety. The Location Intelligence and sensor fusion team develops core signal processing / positioning algorithms. These algorithms process signals from GPS/GNSS, inertial sensors, etc to improve location accuracy and provide helpful safety inferences like harsh braking, crash, etc.
The core team is comprised of signal processing engineers, supported by mobile and infrastructure groups. The following blog post highlights the type of work done by this team.