Aurora delivers the benefits of self-driving technology safely, quickly, and broadly.
San Francisco, CA, US
Bringing self-driving vehicles to our roads is the most transformative opportunity of our generation. Aurora is taking a fresh start with the development of self-driving technology, combining excellence in AI, rigorous engineering, and a team with decades of experience building robots that work.
Led by a team of seasoned experts, our mission is to deliver the benefits of self-driving technology safely, quickly, and broadly. We are designing the software and hardware to power the transportation of our future that will make our roads safer, give more people access to mobility, and reduce congestion and pollution in cities - improving the quality of life for all. The challenge in what we are endeavoring to achieve is transcendent; we are developing perhaps the world's most complex computing system and asking it to perform the task of transporting and keeping safe our most precious asset: human life.
Aurora hires people who are excited to build the future of transportation. We’re looking for people who are as excited as we are to solve these complex problems and make this tremendous impact on our future, and who want to be surrounded by great people while we do it. We are searching for Localization and Mapping Engineers for our Palo Alto or San Francisco office. This candidate will help develop the core algorithms for pose estimation using a variety of sensors and build large scale data processing pipelines for building maps.
- Invent, design and develop real time pose estimation and inertial navigation algorithms that run on the autonomous vehicle
- Invent, design and develop offline algorithms to process large scale sensor data to produce maps used for pose estimation
- Analyze and architect safety and redundancy in the localization system
- MS or PhD in Robotics, Computer Science, or a related field
- 4+ years of work experience
- Excellent C++ programming and software design skills
- Previous work on state estimation problems in a team environment
- Great grasp of linear algebra, probability theory, optimization, and basic geometric algorithms
- Experience with filtering algorithms (i.e.- Kalman filters, particle filters, etc.)
- Experience with sensors including IMU, LIDAR, radar, and cameras
- Safety background - worked in an environment where safety was a priority
- Strong mathematical foundation
- Previous experience in the autonomous vehicle space
Working at Aurora
Our work has real purpose. Delivering the benefits of self-driving will save lives around the world, expand access to transportation, revitalize cities, and give people time back every day.
We’re one team. We’re inspired by the challenge of what we’re solving and the impact our work will have on society. Our camaraderie is built on respect for our work and the fundamental belief our success will be a result of working together.
The Founding Team
Aurora has assembled the most experienced leadership team in the space. Chris Urmson helped lead Carnegie Mellon’s efforts in Darpa’s Grand Challenges, then was a founding member of Google’s self-driving team. Sterling Anderson developed MIT'S Intelligent CoPilot, then led the team that delivered Tesla Autopilot. Drew Bagnell, also a Carnegie Mellon alum, is a machine learning expert who helped build Uber’s autonomy effort. At Aurora, these three continue to bring experts from all areas of the industry to the team. We are funded by Amazon, T Rowe Price, and some of Silicon Valley’s best venture capital firms, including Sequoia, Greylock and Index Ventures.