Autonomy Algorithm Engineer

IOEBXHOVLHC Apply for this position and join us in building Europe’s next generation infrastructure. Our values are rooted in responsibility, readiness, and long term thinking. We believe sovereignty must be designed into systems from the start. Readiness is achieved through capability, not procurement. Autonomy is infrastructure, not a feature. We build with the understanding that modern systems carry long term consequences. That is why we prioritise reliability over novelty, integration over isolation, and sustained capability over short term advantage. Apply for this position Position Role Overview Develop the core state estimation, navigation, control, and mission-level autonomy algorithms enabling fully autonomous UAV flight. 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. Reports to: Head od Software & AI ‍

Key Responsibilities
State Estimation & GPS-Denied Navigation
• Design Kalman filter (extended and unscented) and factor-graph estimators fusing inertial, GPS, vision, barometer and radio-based positioning into a robust six
- degree-of-freedom pose estimate.
• Build and tune visual-inertial odometry and visual SLAM (simultaneous localisation and mapping) pipelines
- ORB-SLAM3, VINS-Fusion, LIO-SAM or custom
- for localisation in GPS-denied and contested environments.
• Ensure the estimator degrades gracefully and recovers from sensor dropout, GPS jamming and spoofing, including data-driven detection of these conditions to trigger fallback modes.
• Where it measurably beats the classical baseline, introduce learned components into the estimator: adaptive noise models, inertial error correction or neural feature matching. Flight Dynamics & Trajectory Algorithms
• Develop motion prediction, trajectory estimation and adaptive filtering for dynamic flight, sensor degradation and environmental uncertainty.
• Create estimators supporting target tracking, collision avoidance and cooperative swarm behaviour, interfacing with the flight controller over MAVLink/PX4.
• Apply learned or hybrid dynamics models where physics-based models under
- perform: aggressive manoeuvring, aerodynamic interaction or degraded actuation. Mission-Level Autonomy
• Design mission behaviours — persistent surveillance loiters, autonomous threat
- response replanning, return-to-safe-area under sensor failure or loss of communications
- and adapt them in flight as sensors degrade, threats emerge or target assignments change.
• Apply reinforcement learning to mission decisions taken on incomplete information (adaptive loiter, sensor tasking, threat response), feeding the estimator's own uncertainty into the decision rather than assuming perfect knowledge of the world.
• Train those policies in simulation, transfer them to hardware, and measure them honestly against conventional rule-based planners before they fly.
• Bound every learned behaviour with hard safety limits and a deterministic fallback, so aircraft behaviour stays predictable and certifiable.
• Develop terminal guidance algorithms for precision terminal-phase operations, including proportional and augmented navigation under high-dynamic flight.
• Integrate geofencing, airspace deconfliction and dynamic no-fly zone enforcement to EASA U-Space requirements. Integration & Validation
• Integrate estimation modules with the flight stack (PX4/ArduPilot) and ROS 2 autonomy pipelines.
• Validate in simulation (Gazebo, MATLAB/Simulink, PX4 software-in-the-loop) and on hardware-in-the-loop rigs before field trials.
• Deploy any neural inference to onboard computers (NVIDIA Jetson class) within real-time, power and thermal budgets.
• Analyse telemetry against measurable targets
- localisation drift, convergence time, robustness to sensor failure
- and iterate.
• Document designs, derivations and interfaces to support certification. ‍ ‍ ‍Required Experience & Skills
• Master's or PhD in Robotics, Control Systems, Electrical Engineering, Computer Science or a related field.
• 3+ years developing state estimation or autonomous navigation for drones, robotics or autonomous vehicles.
• Proven Kalman filter design (extended and unscented), sensor fusion architecture and probabilistic motion modelling.
• Deep knowledge of SLAM
- visual, LiDAR or fused
- and visual odometry.
• C++ and C (Eigen, Ceres, GTSAM) and Python (NumPy, SciPy).
• Working knowledge of machine learning and practical use of PyTorch or similar: building datasets, training, validating, and judging whether a model genuinely generalises.
• Understanding of reinforcement learning basics
- framing a decision problem, designing a reward, choosing between value
- and policy-based methods
- plus the judgement to recognise when a conventional algorithm is the better answer.
• Simulation and hardware-in-the-loop testing (MATLAB/Simulink, Gazebo, PX4).
• Familiarity with PX4/ArduPilot flight stacks and the MAVLink protocol.
• Strong mathematics: linear algebra, probability, numerical methods, optimisation, control theory.
• Version control discipline (Git/GitLab).
• English at upper-intermediate level or above, and the ability to work with adjacent engineering teams.
• National of a NATO member state, or of Australia, Japan, South Korea, New Zealand or Ukraine.
• Clean criminal record. Preferred
• ROS 2: package development, transform trees, sensor integration, data pipelines.
• Hands-on reinforcement learning for robotics
- training policies with standard algorithms and frameworks (for example PPO or SAC in Gymnasium, Stable
- Baselines3 or Isaac Lab) — or imitation learning to bootstrap policies from demonstrations.
• Sim-to-real transfer: domain randomisation, system identification, curriculum learning.
• Swarm work of any kind: multi-agent localisation, distributed optimisation, or multi
- agent reinforcement learning.
• Learning-augmented estimation: differentiable filters, learned inertial odometry, or neural SLAM front-ends.
• Running neural inference efficiently on embedded GPUs (Jetson Orin, TensorRT, quantisation).
• Tightly-coupled visual-inertial odometry (VINS-Mono, Kimera, OpenVINS) and factor graph optimisation (GTSAM, g2o).
• Radio-based positioning (ultra-wideband, pseudolite) as a supplementary navigation source.
• Real-time operating systems (FreeRTOS, Zephyr, NuttX) for hard real-time estimation loops.
• Safety-critical standards: DO-178C, MISRA C/C++, STANAG 4671, and EASA guidance on machine learning in aviation.
• Publications at ICRA, IROS, CoRL or IEEE Transactions on Robotics; open-source contributions to SLAM, estimation or reinforcement learning projects. ‍