Staff Engineer, State Estimation (Dallas/San Diego/Boston/DC/San Fran)

Responsibilities

• 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 (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 (FPGAs).
• Experience transitioning navigation solutions from research into production environments.