Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.

With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai

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.