Field AI is transforming how robots interact with the real world by building risk-aware, reliable, and field-ready AI systems that address complex challenges in robotics, unlocking the full potential of embodied intelligence. The job specifically focuses on building the estimation and navigation stack for legged and humanoid robots.
Requirements
- Strong fundamentals in estimation & sensor fusion (EKF/UKF, error-state, observability/consistency, covariance tuning).
- Hands-on with IMUs (strapdown mechanization, bias/scale, coning/sculling) and GNSS/GPS/RTK (loosely vs tightly coupled INS).
- Experience with legged-robot proprioception: joint encoders, foot contact/force sensors; using kinematic/dynamic constraints in estimators.
- Proficiency in modern C++ (14/17/20) on Linux; Python for tooling, analysis, and log processing.
- Comfort with SO(3)/SE(3), Lie-group math, and non-linear optimization.
- Integration with at least two of: cameras (VIO), LiDAR (LIO/scan-matching), UWB, magnetometer/barometer, radar.
- Familiarity with ROS 1/ROS 2, CMake/Bazel, Docker, CI/CD, and reproducible experiments.
Responsibilities
- Design and tune EKF/UKF error-state filters for floating-base pose/velocity, COM, IMU biases, and contact states.
- Fuse IMU, joint encoders, foot F/T & contact sensors; implement ZUPT/ZARU, slip handling, and kinematic/dynamic constraints.
- Expose clean interfaces (frames/timestamps/covariances) to whole-body control and footstep planning.
- Stand up VIO/LIO pipelines (stereo/RGB-D + LiDAR) for GPS-denied operation, with map-based relocalization and loop closure.
- Add global aids—GNSS/RTK, UWB beacons, prior maps—and blend filtering with factor-graph smoothing when advantageous.
- Own time sync (PTP/Chrony/hardware triggers) and multi-sensor calibration (Allan variance for IMU, camera-IMU/LiDAR-IMU/base extrinsics, encoder offsets).
- Build health monitoring, FDIR, and graceful-degradation behaviors for harsh terrain and intermittent sensors.
Other
- Proven track record shipping research-to-production algorithms on real robots with field test cycles.
- BS/MS/PhD in Robotics/EE/CS/AE or equivalent practical experience.
- Open to exploring a hybrid or remote option.
- Ability to work with a world-class team that thrives on creativity, resilience, and bold thinking.
- Eagerness to tackle tough, uncharted questions and challenge the status quo.
