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Lyft // on-demand ride-sharing
 
Engineering, Full Time    Palo Alto, CA    Posted: Saturday, February 15, 2020
 
   
 
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At Lyft, community is what we are & its what we do. Its what makes us different. To create the best ride for all, we start in our own community by creating an open, inclusive, & diverse organization where all team members are recognized for what they bring. From day one, Lyfts mission has been to improve peoples lives with the worlds best transportation. And self-driving cars are critical to that mission: they can make our streets safer, cities greener, & traffic a thing of the past. Thats why we started Level 5, our self-driving division, where were building a self-driving system to operate on the Lyft network.

Level 5 is looking for doers & creative problem solvers to join us in developing the leading self-driving system for ride sharing. Our team members come from diverse backgrounds & areas of expertise, & each has the opportunity to have an outsized influence on the future of our technology. Our world-class software & hardware experts work in brand new garages & labs in Palo Alto, California, & offices in London, England & Munich, Germany. And were moving at an incredible pace: were currently servicing employee rides in our test vehicles on the Lyft app. Learn more at lyft.com/level5.

As part of the Perception & Autonomy Team, you will be interacting on a daily basis with other software engineers to tackle highly advanced perception challenges. Eventually we expect all Autonomy Team members to work on a variety of problems across the autonomy space; however, with a focus on perception, your work will initially involve turning our constant flow of sensor data into a model of the world. For this position, we are looking for a software engineer with the ability to master the problem of object detection & analysis using advanced algorithms operating on LiDAR data & ancillary signals from other modalities. The ideal candidate will have a strong autonomous vehicle domain knowledge, expertise in traditional computer vision & modern, deep learned approaches to object detection & segmentation.

 
 
 
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