Engineer, State Estimation (dallas/san Diego/boston/ Dc/ San Fran)

Shield AI Shield AI · Defense · Dallas, TX +4 · V-BAT Engineering - Software

This role focuses on developing and implementing real-time state estimation algorithms, including inertial navigation and sensor fusion, for autonomous UAVs operating in GPS-denied environments. The engineer will integrate multi-sensor data into robust estimation frameworks and optimize sensor processing pipelines for accuracy and fault tolerance, supporting the transition of navigation solutions from research to production.

What you'd actually do

  1. Develop and implement real-time state estimation algorithms including inertial navigation, sensor fusion, and alternative navigation methods for GPS-denied or degraded environments.
  2. Integrate data from IMUs, GNSS receivers, visual odometry, magnetometers, barometers, and radar into robust estimation frameworks.
  3. Design sensor processing pipelines focused on accuracy, robustness, and system-level fault tolerance.
  4. Collaborate with autonomy, software, and hardware teams to ensure end-to-end integration of navigation and PNT systems.
  5. Conduct simulation, lab testing, and field trials to evaluate algorithm performance under real-world conditions.

Skills

Required

  • 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

Nice to have

  • 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)
  • 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

What the JD emphasized

  • real-time
  • GPS-denied or degraded environments
  • robust estimation frameworks
  • system-level fault tolerance
  • transitioning navigation solutions from research into production environments

Other signals

  • autonomous UAV operations
  • GPS-denied or degraded environments
  • real-time sensor processing pipelines
  • robust state estimation
  • precision navigation solutions