Senior Estimation & Navigation Engineer

Senior Estimation & Navigation Engineer — Autonomous Aerial Systems

About the Role

We're seeking a Senior State Estimation & Sensor Fusion Engineer to build the localization and navigation backbone for our autonomous aerial platforms, spanning multicopters and VTOL aircraft. You'll own the design, implementation, and validation of estimation pipelines that fuse data across multiple onboard sensors to deliver dependable state information even in degraded or GPS-denied conditions.

This is a highly collaborative role: you'll partner closely with controls, perception, autonomy, and embedded software engineers to make sure your estimation outputs plug directly into the flight control stack and hold up under real-time constraints.

What You'll Do

You'll build and maintain the core state estimation algorithms powering our aerial platforms, fusing inputs from IMUs, GNSS, magnetometers, barometers, airspeed sensors, rangefinders, cameras, LiDAR, and radar. A major focus will be hardening the estimator against sensor dropout, degraded environments, GPS-denial, and aggressive flight dynamics including developing calibration routines for both inertial and exteroceptive sensors, and building in fault detection, health monitoring, and redundancy.

Beyond algorithm development, you'll characterize estimator performance (accuracy, consistency, latency, compute cost), collaborate with controls engineers to make sure your outputs meet their requirements, and build out simulation and hardware-in-the-loop test infrastructure. You'll dig into flight logs to root-cause estimation issues, support live flight testing and field debugging, and keep documentation of algorithms, tuning, and validation results current.

Core Responsibilities

Designing and maintaining state estimation algorithms; fusing IMU, GNSS, magnetometer, barometer, airspeed, rangefinder, camera, LiDAR, and radar data; improving resilience to sensor failure and GPS-denied conditions; building sensor calibration procedures; implementing fault detection and redundancy strategies; benchmarking estimator accuracy and performance; partnering with controls teams on integration; developing simulation and SIL/HIL test campaigns; analyzing flight logs; supporting field flight tests; and documenting algorithms and validation results.

Required Qualifications

A BSc or MSc in Robotics, Aerospace Engineering, Electrical Engineering, Computer Engineering, Computer Science, or a related discipline, along with a solid grounding in estimation theory and probabilistic robotics. You should have hands-on experience with EKF, UKF, ESKF, complementary filtering, and nonlinear observers, plus strong command of coordinate frames, rigid body kinematics, rotation representations (quaternions, rotation matrices, Lie groups), inertial navigation, GNSS positioning, and IMU modeling/calibration. Excellent C++ skills and experience building software for Linux-based embedded systems are essential, as are solid version control habits and strong analytical/debugging instincts.

Preferred Qualifications

Experience with visual-inertial odometry, visual SLAM, or LiDAR SLAM is a plus, as is familiarity with factor graph optimization and tools like Ceres or GTSAM. Exposure to PX4, ArduPilot, ROS 2, MAVLink, camera/extrinsic calibration, time synchronization, stochastic modeling, embedded deployment on constrained hardware, and simulation platforms like Gazebo, Isaac Sim, or CoppeliaSim would all be valuable.

Nice to Have

Real-world UAV flight testing experience, familiarity with contested or GPS-denied navigation, multi-sensor timestamp synchronization, safety-critical development practices, and any publications or open-source contributions in robotics or autonomy.

Technical Toolkit

Languages: C++, Python. Math foundation: linear algebra, probability, numerical optimization, estimation theory. Middleware: ROS 2, DDS. Flight stacks: PX4, ArduPilot. Tools: Git, CMake, Docker. Simulation: Gazebo, Isaac Sim, and optionally MATLAB/Simulink.

What You'll Be Working On

High-performance estimation for multicopters and VTOL aircraft, sensor fusion across diverse sensing modalities, resilient GPS-denied navigation, flight-ready software deployed on embedded companion computers and flight controllers, and validation through simulation, SIL/HIL testing, and live flight data all in close coordination with controls, autonomy, perception, and systems engineering.