Senior / Staff Software Engineer, Localization

Waabi Waabi · Robotics · San Francisco, CA +2 · Remote · Software Engineering

Senior/Staff Software Engineer on the Localization team at Waabi, focusing on architecting and developing highly precise, robust state estimation systems for autonomous vehicles. This role involves designing algorithms for multi-sensor fusion, optimizing Rust code for real-time vehicle compute, and building evaluation frameworks. The position requires deep expertise in probabilistic robotics, state estimation, SLAM, and sensor data processing, with a strong emphasis on deploying these algorithms in real-world autonomous driving systems.

What you'd actually do

  1. Act as a deep domain expert in state estimation, pushing the boundaries of what is possible in real-time vehicle localization.
  2. Design, implement, and optimize robust algorithms for multi-sensor fusion leveraging IMU, LiDAR, Radar, Camera, GNSS, wheel encoders, etc.
  3. Architect and develop mathematical models to be used in factor graph optimization, Kalman filters, etc.
  4. Develop highly optimized, low-latency Rust code that runs directly on the vehicle's various compute devices in real-time.
  5. Partner with the Perception and Mapping teams to tightly couple map data and semantic landmarks into the localization pipeline.

Skills

Required

  • 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.

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.

What the JD emphasized

  • highly precise, robust state estimation systems
  • real-time, centimeter-accurate pose estimation
  • deploying complex state estimation algorithms onto physical robots or autonomous vehicles operating in the real world

Other signals

  • AI-first approach
  • state estimation systems
  • sensor fusion
  • real-time, centimeter-accurate pose estimation