Requirements
- Deep knowledge in vision for robotic systems
- Hands-on experience implementing SLAM pipelines in C++ and Python — you have built and tuned these systems end-to-end, not just integrated existing libraries
- Strong working knowledge of modern SLAM frameworks: ORB-SLAM3, RTAB-Map, Cartographer, LIO-SAM, or KISS-ICP — and the ability to extend or rewrite core components when needed
- Experience with neural or learned SLAM approaches (DROID-SLAM, iMAP, NeRF-SLAM)
- Experience with legged or humanoid-specific odometry challenges
- Comfortable with probabilistic state estimation, Kalman filtering (EKF/UKF), and particle filters as they apply to real-time localization under uncertainty
- Familiar with loop closure detection methods, place recognition networks and strategies for long-term map consistency in changing environments
- Hands-on experience with simulation environments such as Isaac Lab, MuJoCo for development, testing, and sim-to-real validation
