Staff Engineer, State Estimation (R3260)
Shield AI
What you'll do:
- Develop and implement real-time state estimation algorithms including inertial navigation, sensor fusion, and alternative navigation methods for GPS-denied or degraded environments.
- Integrate data from IMUs, GNSS receivers, visual odometry, magnetometers, barometers, and radar into robust estimation frameworks.
- Design sensor processing pipelines focused on accuracy, robustness, and system-level fault tolerance.
- Collaborate with autonomy, software, and hardware teams to ensure end-to-end integration of navigation and PNT systems.
- Conduct simulation, lab testing, and field trials to evaluate algorithm performance under real-world conditions.
- Stay current on advancements in state estimation and navigation technologies and help adapt new innovations into deployable solutions.
Required qualifications:
- Typically requires a minimum of 7 years of relevant experience with a bachelor’s degree; or 6 years with a master’s degree; or 4 years with a PhD; or equivalent practical experience.
- Experience developing and deploying real-time navigation or sensor fusion algorithms using IMUs, GPS, or other sensors.
- Strong understanding of filtering and estimation techniques (e.g., Kalman filters, EKF, UKF, particle filters).
- Proficient in C++11 or newer in real-time environments.
- Comfortable working in Linux, with experience using standard command-line tools and scripting.
- Strong written and verbal communication skills with a collaborative mindset.
- Demonstrated success working in fast-paced development cycles and delivering high-quality results.
Preferred qualifications:
- Experience implementing inertial navigation algorithms in degraded or GPS-denied conditions.
- Exposure to visual odometry or computer vision-based navigation approaches.
- Experience optimizing code for performance on compute-constrained platforms.
- Familiarity with CUDA or hardware acceleration techniques (e.g., FPGAs).
- Experience transitioning navigation solutions from research into production environments.