About the Role
A Staff-level Engineering role at Uber is special. Engineers at this level represent the top 5% of Engineers at Uber and will have a deep impact across a wide variety of business and technology decisions spanning multiple projects and at times, locations.
We are looking for an experienced technologist who can guide signal processing and machine learning research projects, leveraging our enormous raw location and sensor data streams from driver phones to provide a reliable platform for other teams to understand the physical world. You will help shape the company's ML strategy, collaborating with multiple teams including Safety, Marketplace, and Maps.
As a Machine Learning engineer on the team, you will be working with a wide range of sensor data (location, motion, camera, etc) to develop inferences that enable some of the most important parts of Uber's business - Fares, Matching, Safety, Pickup Experience, and more.
What You'll Do
- Lead research projects spanning signal processing, machine learning, and high-performance engineering systems to deliver critical insight to core business flows
- Identify and advocate for investment in foundational areas, spanning hardware through research (i.e. roadmaps for machine learning infrastructure, forward-looking partnerships with universities)
- 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
- MS or PhD in CS, EE or related disciplines. PhD preferred.
- 10+ years experience with a track record of shipping high-impact research projects at a premier technology company
- Deep knowledge of and track record of research in machine learning (e.g. sequential models, classification, deep learning)
- Experience with machine learning infrastructure and shipping models at scale
- Ability to communicate complex black-box models to cross-functional stakeholders
- Collaborative attitude and experience working in a cross-functional team
- Excellent programming and algorithmic skills (we mainly use Java & Python)
Bonus Points for
- Depth in sensors and hardware, particularly prior experience working with Inertial Measurement Unit (IMU) and GPS
- Experience working with data at scale, including experience with some or all of the following: HDFS, Cassandra, Kafka, Flink, Samza, Spark, EMR
About the Team
Uber is deeply rooted in the physical world -- our business requires a clear understanding of complicated real-world interactions and behaviors. The Sensing and Perception team seeks to understand these interactions about every trip through the use of sensors.
We create actionable insights that our partner product teams (Rider, Driver, Eats, Safety et al) use to improve customer and trip experiences. We do this by researching new models and algorithms and building platforms to serve our insights to customers at Uber scale.
This team is responsible for collecting and processing sensor data including GPS, IMU, Barometer, and more across phones and other Driver devices. Our team owns the core location pipeline ("Blue-Dot") at Uber that drives decisions across systems like ETA, Traffic, Routing, Safety, Fares, Matching and more.
We are part of a newly created org - UberAI - whose mission is to "to transform data into intelligence by pushing the frontiers of research, developing high quality scalable platforms, and collaborating on innovative applications."
Come join our team!
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.