Engineer, State Estimation

As a State Estimation Engineer, you will play a critical role on the GNC team, contributing to the development, optimisation, and deployment of advanced sensor fusion and navigation algorithms for autonomous UAV operations in dynamic and contested environments.

Your work will support the transition of cutting-edge research into fielded capabilities, helping Shield AI deliver precision navigation solutions for mission-critical applications.

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.

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 years 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 optimising 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.