Waabi
Senior / Staff Software Engineer, Localization
San Francisco, CAfull timeunspecifiedtransportation
Rust — C++ — SLAM — Sensor Fusion — State Estimation — Kalman Filters — Point Cloud Registration — Python — Factor Graph Optimization — Real-time Computing
Description
The Localization team at Waabi is responsible for answering one of the most critical questions in autonomous driving: exactly where is the vehicle right now? As a Senior or Staff Software Engineer on the Localization team, you will be a domain expert architecting the highly precise, robust state estimation systems that keep our robotaxis and 80,000lb trucks safely on the road. You will design algorithms that seamlessly fuse data across a complex sensor suite to provide real-time, centimeter-accurate pose estimation, even in degraded or GPS-denied environments. You will collaborate with world-renowned engineers and scientists to merge traditional robotics state estimation with Waabi's AI-first approach. You will... - Act as a deep domain expert in state estimation, pushing the boundaries of what is possible in real-time vehicle localization. - Design, implement, and optimize robust algorithms for multi-sensor fusion leveraging IMU, LiDAR, Radar, Camera, GNSS, wheel encoders, etc. - Architect and develop mathematical models to be used in factor graph optimization, Kalman filters, etc. - Develop highly optimized, low-latency Rust code that runs directly on the vehicle's various compute devices in real-time. - Partner with the Perception and Mapping teams to tightly couple map data and semantic landmarks into the localization pipeline. - Build rigorous evaluation frameworks to measure localization accuracy, integrity, fault tolerance across millions of miles in Waabi World (our simulation platform) and on physical test tracks. Qualifications: - BS, MS, or PhD in Robotics, Computer Science, Aerospace/Electrical Engineering, or related field, with a minimum for 5 years of industry experience. - Deep, rigorous domain expertise in probabilistic robotics, state estimation, and 3D geometry (Gaussian estimation, filtering, smoothing, and mapping). - Proven experience building and optimizing online and/or offline Simultaneous Localization and Mapping (SLAM) systems, including deep knowledge of point-cloud registration algorithms (e.g. ICP). - Extensive hands-on experience processing and fusing data from physical sensors (IMU, LiDAR, Radar, GNSS, etc.). - Exceptional systems-level programming skills in modern C++ and/or Rust, with a strong understanding of memory management, concurrency, and real-time computing constraints. - Proficiency in python for data analysis, prototyping, and tooling. - Strong mathematical foundation in linear algebra, calculus, and probability theory. - A proven track record of deploying complex state estimation algorithms onto physical robots or autonomous vehicles operating in the real world. Bonus/nice to have: - Familiarity with industry-standard optimization libraries (e.g., GTSAM, Ceres Solver, g2o). - Experience with "learned localization" - applying deep learning and AI/ML models to improve traditional state estimation and feature matching. - Experience with high-speed highway autonomous driving constraints.