OUR PROJECT
The Principal Navigation Engineer will support a next-generation autonomous robotic spacecraft designed to perform complex on-orbit missions. The space craft will operate in a GPS-denied orbital environment relying entirely on onboard sensing, estimation, and autonomous decision making. This program requires advanced onboard navigation solutions capable of fusing multiple sensors (LIDAR, radar, vision) to generate high-confidence state estimates for relative position, velocity, and attitude during close-proximity maneuvers. The Navigation Filter Engineer will play a critical role in enabling safe and autonomous docking through robust, high-fidelity state estimation.
This is a long-term contract position that will be 100% remote requiring occasional travel to Houston, TX. You can expect very competitive pay.
WHO WE ARE LOOKING FOR
We are looking for a Principal Navigation Engineer (Kalman Filters) who willlead the design, implementation, and validation of advanced state estimation algorithms for spacecraft relative motion. This individual will develop Kalman-based and nonlinear filtering solutions that fuse real-time sensor data to support autonomous rendezvous and collision avoidance. The role requires deep experience in estimation theory, orbital dynamics, and sensor fusion within safety-critical aerospace systems.
QUALIFICATIONS
- Background in estimation theory and nonlinear filtering, with experience implementing Kalman Filters (KF), Extended Kalman Filters (EKF), Unscented Kalman Filters (UKF), and Particle Filters.
- Experience applying filters to orbital mechanics and relative motion dynamics with proven expertise in multi-sensor fusion (LIDAR, radar, vision) for high-accuracy state estimation.
- Proficient in MATLAB/Simulink, Python, or C++, with experience in high-fidelity simulation and SIL/HIL validation.
Effective written and verbal communication skills are absolutely required for this role. You must be able to work LEGALLY in the United States as NO SPONSORSHIP will be provided. NO 3rd PARTIES.