Drone Navigation Systems Engineer

Drone Mid-level (L4 equivalent) 2-5 years

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To generate precise rejection-prevention feedback and application strategy, tell us where you're targeting.

Anduril

Skydio

Shield AI

Zipline

DJI

AeroVironment

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The job description

Tech stack. Sensor fusion, Kalman filters (EKF/UKF), GPS-denied navigation, visual-inertial odometry, IMU/GNSS integration, RTK/PPK, C++/Python, ROS 2, integrity monitoring

About the role

You will join a drone technology company owning the question every autonomous flight depends on: where am I, exactly, right now? You design navigation systems that fuse GNSS, inertial, visual, and other sensors into a trustworthy position and attitude solution, including when GPS disappears entirely. Your estimators are the quiet foundation under every autonomous mission the company flies, and their integrity is what makes autonomy safe to deploy. You will also serve as the navigation authority on flight test campaigns, analyzing estimator performance after each flight and driving tuning changes backed by quantitative comparison against ground truth.

What you will achieve

Design and tune multi-sensor fusion estimators that deliver the accuracy and integrity each mission requires, in both GNSS-available and GPS-denied environments.
Reduce navigation failures by characterizing sensor error models and building integrity monitoring that detects and excludes faulty measurements before they corrupt the solution.
Enable operations in challenging environments such as urban canyons, indoor spaces, and under-canopy areas through visual-inertial odometry and alternative navigation aids.
Improve estimator robustness with systematic tuning and validation against ground-truth data from motion capture or RTK reference systems.
Deliver navigation software with performance documentation and test evidence that certification teams and customers can rely on.

What you will bring

Must-haves

2-5 years of experience in navigation, guidance, or state estimation for UAVs, robotics, or aerospace systems.
Deep understanding of Kalman filtering, including EKF and UKF formulations, and nonlinear estimation applied to real sensor data.
Hands-on experience with IMU and GNSS integration, including RTK and PPK workflows and the major GNSS error sources.
Working knowledge of visual-inertial odometry or SLAM-based localization approaches for mobile platforms.
Proficiency in C++ and Python for estimator implementation, tuning, and analysis.
Experience with sensor calibration: IMU intrinsics, camera-IMU extrinsic calibration, and magnetometer calibration.
Bachelor's or Master's degree in aerospace, robotics, or related engineering.

Nice-to-haves

Experience with alternative positioning sources such as terrain-relative navigation, signals of opportunity, or ultra-wideband ranging.
Background in integrity monitoring concepts, including RAIM-like methods adapted for drones.
Familiarity with PX4 EKF2 internals or similar production estimator implementations.
Experience with tightly coupled GNSS and inertial integration architectures.