Foundation is seeking an AI Engineer to build and maintain the foundational SLAM system for a humanoid robot operating in unstructured, real-world environments. You will focus on creating custom, embodied SLAM solutions from the ground up to enable centimeter-level precision localization and mapping in dynamic settings.

What You'll Do

Build and maintain accurate, real-time maps of dynamic environments for a humanoid robot.
Simultaneously localize the robot with centimeter-level precision.
Develop custom, embodied SLAM solutions from the ground up for deployment across varied terrains like floors, stairs, cluttered rooms, and outdoors.
Scale the SLAM infrastructure layer to be production-ready to support upstream systems like perception, planning, and manipulation.

What We're Looking For

Deep knowledge in vision for robotic systems.
Hands-on experience implementing SLAM pipelines in C++ and Python end-to-end.
Strong working knowledge of modern SLAM frameworks (e.g., ORB-SLAM3, RTAB-Map, Cartographer, LIO-SAM, KISS-ICP) and ability to extend or rewrite core components.
Comfortable with probabilistic state estimation, Kalman filtering (EKF/UKF), and particle filters for real-time localization.
Familiar with loop closure detection methods and place recognition networks for long-term map consistency.
Hands-on experience with simulation environments (e.g., Isaac Lab, MuJoCo) for development, testing, and sim-to-real validation.

Nice to Have

Experience with neural or learned SLAM approaches (e.g., DROID-SLAM, iMAP, NeRF-SLAM).
Experience with legged or humanoid-specific odometry challenges.
Experience with multi-session and multi-agent mapping.