Software Engineer, Localization (Autonomy)

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From day one, Lyft’s mission has been to improve people’s lives with the world’s best transportation. And self-driving cars are critical to that mission: they can make our streets safer, cities greener, and traffic a thing of the past. That’s why we started Level 5, our self-driving division, where we’re building a self-driving system to operate on the Lyft network.

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

As a localization engineer on the autonomy team, you will be responsible for working on the subsystems that provide a highly accurate and up-to-date pose for the autonomous vehicle. This will involve working on cutting edge localization and mapping algorithms to bring them up to production quality and stability. You will be responsible for characterizing the accuracy of the localization system, running experiments on the car, and continuing to improve our ever-evolving production localization system. For this position, we are looking for a software engineer with the ability to understand autonomous vehicles in general and strong level of expertise in localization and/or odometry.

Responsibilities:


  • Work on core algorithms for localizing against an HD geometric map, including algorithms for lidar-based global scan matching, lidar-based odometry, “dead reckoning” from IMU data, visual odometry, GPS, etc.

  • Work closely with the mapping team to ensure maps are high quality and up to date

  • Work with the perception team on detecting stale or inaccurate maps at run time

  • Implement real-time algorithms (< 10 milliseconds) on CPU/GPU in C++

  • Build tools and infrastructure to evaluate the performance of the localization stack and track it over time


Experience & Skills:

  • Ability to produce production-quality C++

  • Strong background in mathematics, linear algebra, geometry, probability, and optimization

  • Ability to build highly complex localization software that runs large-scale optimization problems in real-time to perform pose estimation and odometry from various sensor data sources

  • Bachelor's degree or higher in Computer Science, Electrical Engineering, or related field

  • Ability to work in a fast-paced environment and collaborate across teams and disciplines

  • Openness to new / different ideas. Ability to evaluate multiple approaches and choose the best one based on first principles


Nice to Have:

  • 2+ years experience working in a related role

  • 5+ years developing in C++

  • Hands on experience with localization in a real outdoor robotics system

  • Experience with building HD geometric maps from sensor data of various modalities

  • Experience with visual odometry methods such as using structure from motion or stereo camera data for ego motion estimation


Lyft is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Lyft does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender-identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Pursuant to the San Francisco Fair Chance Ordinance and other similar state laws and local ordinances, and its internal policy, Lyft will also consider for employment qualified applicants with arrest and conviction records.


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