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Shield AIShield AIDallas, TX

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

Develops real-time state estimation algorithms, sensor fusion, and navigation for autonomous UAVs in GPS-denied environments. Requires 3+ years experience, C++ proficiency in Linux, and bachelor's degree.

120k – 250k/yr
On-site3+ YOEEmbedded Engineering

About the role

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 3 years of relevant experience with a bachelor’s degree; or 2 years with a master’s degree; or 1 year with a PhD; or equivalent practical experience.
  • Familiarity with algorithms.
  • 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 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.

Skills

C++LinuxKalman FiltersEkfUkfParticle FiltersSensor FusionInertial NavigationVisual OdometryCUDA

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