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

Founded in 2015, Shield AI is a venture-backed deep-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include the V-BAT and X-BAT aircraft, Hivemind Enterprise, and the Hivemind Vision product lines. With offices and facilities across the U.S., Europe, the Middle East, and the Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: As a Staff State Estimation Engineer, you will play a critical role on the GNC team, contributing to the development, optimization, and deployment of advanced sensor fusion and navigation algorithms for autonomous UAV operations in dynamic and contested environments. You will help design real-time sensor processing pipelines, integrate multi-sensor data for robust state estimation, and collaborate closely with autonomy researchers, software engineers, and hardware teams to ensure high system performance and reliability. Your work will support the transition of cutting-edge research into fielded capabilities, helping Shield AI deliver precision navigation solutions for mission-critical applications. #LI-SM1 #LD Full-time regular employee offer package: Pay within range listed + Bonus + Benefits + Equity Temporary employee offer package: Pay within range listed above + temporary benefits package (applicable after 60 days of employment) Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information. ### Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know. 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.

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