In this role, you’ll:
• Develop and run large scale optimization and estimation routines. Replay and simulate using existing log data to characterize performance, tune parameters, identify failures and provide performance guarantees
• Integrate sensors into pose estimation and inertial navigation algorithms for deployment on the self driving car. Examples include Kalman filters, EKF, UKF, particle filters, etc.
• Investigate sensor and filter performance issues. Debug through replay, simulation, residual-analysis and on-car testing
At a minimum we’d like you to have:
• PhD or M.S. degree with equivalent experience in fields such as estimation, optimization, statistics, modeling, dynamics, control, etc.
• 5+ years of C++ programming experience
It’s preferred if you have:
• Domain knowledge and implementation experience with Kalman filtering, inertial sensors, and sensor fusion. Real world experience with system integration and deployment
• Experience with real-time robot/vehicle/drone positioning and localization
• System dynamics and kinematics modeling for ground vehicles