Autonomy Algorithm Engineer

LUXUAV designs and manufactures UAV systems in Luxembourg for commercial and security applications across Europe. These systems rely increasingly on onboard perception and AI, developed, trained, and deployed under strict operational, security, and regulatory constraints.

We are building an in‑house, sovereign AI capability. All model development, training, and deployment are performed on‑premise, using proprietary and sensitive aerial datasets. This role exists to ensure that the AI embedded in our systems is reliable, controllable, and fit for operational use.

We are looking for an Autonomy Algorithm Engineer who will join our team to develop the core state estimation, navigation, control, and mission-level autonomy algorithms enabling fully autonomous UAV flight. This role covers SLAM, Visual-Inertial Odometry (VIO), Kalman filter design, GNSS-denied navigation, and mission-level behavioral autonomy for operations in contested, GPS-degraded, and communications-denied environments.

Responsibilities:

State Estimation & GNSS-Denied Navigation:

• Design and implement EKF/UKF-based and factor-graph state estimators integrating IMU, GPS, vision, barometer, and RF positioning for robust 6-DOF pose estimation.
• Develop and tune VIO and visual SLAM pipelines (ORB-SLAM3, VINS-Fusion, LIO-SAM, or custom) for accurate localization in GPS-denied and contested environments.
• Implement resilient localization strategies that gracefully degrade and recover across sensor dropout scenarios, including GPS jamming and spoofing conditions.

Flight Dynamics & Trajectory Algorithms:

• Develop motion prediction, trajectory estimation, and adaptive filtering for dynamic flight conditions, sensor degradation, and environmental uncertainty.
• Create control-theoretic estimators supporting target tracking, collision avoidance, and cooperative swarm behaviors.
• Design and validate observer/estimator architectures interfacing with flight control units (FCUs) via MAVLink/PX4.

Mission-Level Behavioral Autonomy:

• Design mission-level autonomy behaviors: persistent ISR loitering patterns, autonomous threat-response replanning, and return-to-safe-area protocols under sensor failure or communications blackout.
• Implement mission re-planning under uncertainty — adapting navigation and task objectives in response to degraded sensors, emerging threats, or dynamic target assignments.
• Develop time-critical autonomous terminal guidance algorithms for precision terminal-phase operations, including proportional/augmented navigation under high-dynamic flight conditions.
• Integrate geofencing, airspace deconfliction logic, and dynamic no-fly zone enforcement compliant with EASA U-Space requirements.

Integration & Validation:

• Integrate state estimation modules with flight control stacks (PX4/ArduPilot) and ROS2-based autonomy pipelines.
• Validate algorithms in simulation (Gazebo, MATLAB/Simulink, PX4 SITL) and HIL/HITL environments before field trials.
• Analyze telemetry, measure KPIs (localization drift, convergence time, robustness to sensor failure), and iterate improvements.
• Document algorithm designs, mathematical derivations, performance characteristics, and integration interfaces; support certification efforts.

Required Qualifications & Experience:

• Master's or PhD in Robotics, Control Systems, Electrical Engineering, Computer Science, or related field.
• 3+ years developing state estimation or autonomous navigation algorithms for UAVs, robotics, or autonomous vehicles.
• Proven expertise in Kalman filter design (EKF, UKF), sensor fusion architectures, and probabilistic motion models.
• Deep knowledge of SLAM (visual, LiDAR, or sensor-fusion variants) and visual odometry pipelines.
• Proficiency in C++ and C (Eigen, Ceres, GTSAM) and Python (NumPy, SciPy) for algorithm development.
• Applied experience with simulation tools (MATLAB/Simulink, Gazebo, PX4 SITL) and hardware-in-the-loop testing.
• Familiarity with flight control stacks (PX4/ArduPilot) and MAVLink protocol.
• Strong mathematical foundation in linear algebra, probability theory, numerical methods, and control theory.
• Solid version control practices (Git/GitLab).
• English: Upper Intermediate or higher.
• Good communication skills and ability to cooperate with adjacent engineering teams.
• Free criminal record

Preferred Qualifications & Experience:

• Proficiency in ROS2: package development, TF trees, sensor/message integration, data pipelines.
• Experience with swarm coordination, multi-agent localization, and distributed optimization algorithms.
• Familiarity with tightly-coupled VIO systems (VINS-Mono, Kimera, OpenVINS) and resilient GNSS-denied approaches.
• Exposure to factor graph optimization libraries (GTSAM, g2o) for batch and incremental SLAM. Experience with RF-based positioning (UWB, pseudolite) as supplementary navigation sources.
• Experience with real-time operating systems (FreeRTOS, Zephyr, NuttX) and hard real-time constraint design for estimation loops.
• Familiarity with safety-critical standards: DO-178C (software levels DAL A–C), MISRA C/C++, and STANAG 4671 for UAV autonomy modules.
• Track record of publication at ICRA, IROS, IEEE Transactions on Robotics, or equivalent; open-source contributions to SLAM/estimation projects.
• National from a NATO member country or one of the following NATO Indo-Pacific partners: Australia, Japan, South Korea, New Zealand or Ukraine.

If this sounds like an opportunity that interests you, please send us your application. We look forward to hearing from you!

Please note that only shortlisted candidates will be contacted.