ABOUT US:
We are a dynamic technology company developing next-gen solutions for a rapidly evolving world. Our focus currently is on building scalable air defence systems, to Counter Uncrewed Aerial Systems (C-UAS), for protecting our cities and critical assets from threats. In turn, we are laying the foundation of large-scale autonomous interception systems to counter proliferation of hostile UxVs.
Large scale inspections of Dykes, waterways and disaster management are some of the emergent dual applications of our swarm systems.
The Role:
Join us as a Founding UAV Engineer to architect and lead the development of breakthrough aerial swarm technologies. As a founding team member, you will lead the development of core autonomy, coordination, and control systems that enable fleets of UAVs to operate collaboratively in complex real-world scenarios. This role combines deep technical expertise in UAV systems with an entrepreneurial drive to establish foundational engineering practices in a cutting-edge defence company.
Key Responsibilities:
UAV Development
- System configuration, including compute, sensor, IMU, communications, flight physics and control
- Implement SDK integration for advanced autonomy features including Visual Inertial Odometry (VIO), Visual Obstacle Avoidance (VOA), and 3D mapping for GPS-denied navigation.
- Develop custom flight controllers using PX4 autopilot within ROS2 middleware.
- Configure real-time communication systems using DDS (Data Distribution Service), MQTT, and mesh networking protocols for inter-UAV coordination.
- Implement Docker containerization for modular application deployment and testing workflows from simulation to reality.
Agentic & Distributed Swarm Architecture
- Design and implement autonomous agent frameworks where each UAV operates as an independent agent capable of sensing, reasoning, and acting while coordinating with the swarm.
- Develop decentralized coordination protocols using Multi-Agent Reinforcement Learning (MARL) techniques for collision avoidance, formation control, and task allocation.
- Program distributed decision-making algorithms using (for example) Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) for adaptive swarm behavior in dynamic environments.
Testing & Validation
- Define comprehensive testing frameworks using simulation environments (Issac, Gazebo, AirSim) to model large-scale swarm scenarios before hardware deployment.
- Conduct field operations to validate swarm performance under varying environmental conditions and mission requirements.
- Develop safety and compliance procedures ensuring swarm operations meet regulatory standards and safety protocols.
Required Qualifications:
Technical Expertise
- 4+ years of hands-on experience in UAV systems development, flight control, autonomy, or swarm robotics.
- Real-time systems programming in C++ and Python for embedded flight control applications.
- ROS2 expertise including custom node development, message passing, and distributed system architectures.
- Real-time data streaming for telemetry, video feeds, and inter-UAV coordination messages.
- Deep learning frameworks (TensorFlow Lite, PyTorch) for onboard neural network inference and object detection.
- Computer vision algorithms for Visual SLAM, optical flow, and sensor fusion using OpenCV and custom vision pipelines.
- Wireless protocol configuration, such as WiFi mesh, 4G/5G, Microhard radio, and ELRS/Ghost Atto RC systems.
- Linux system administration, such as Ubuntu/Yocto environments, package management, and debugging tools.
Domain Knowledge:
- Knowledge of state-of-the-art hardware and algorithms.
- Expertise in UAV flight dynamics, sensor fusion, navigation systems (IMU, visual odometry), and UAV hardware integration.
- Understanding of wireless communication systems and protocols for UAV-to-UAV and UAV-to-ground communications.
- Background in control theory, real-time systems, and embedded software development.
- Git/GitLab workflow management with CI/CD pipelines for embedded systems development.
Preferred Qualifications:
Agentic & Distributed Swarm Architecture
- Design and implement autonomous agent frameworks where each UAV operates as an independent agent capable of sensing, reasoning, and acting while coordinating with the swarm.
- Develop decentralized coordination protocols using Multi-Agent Reinforcement Learning (MARL) techniques for collision avoidance, formation control, and task allocation.
- Program distributed decision-making algorithms using (for example) Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) for adaptive swarm behavior in dynamic environments.
General
- Field deployment experience in real-world operational environments.
- Experience in leading technical teams or projects from concept through deployment
- Advanced degree (MS/PhD) in Aerospace, Robotics, Computer Science, Mechatronics or a related field. A track record of building real world solutions is preferred over an advanced degree from a top institution.
- Previous founding engineer or CTO experience at technology startups.
- Background in AI/ML applications for autonomous systems, particularly reinforcement learning for multi-agent coordination.
- Security clearance or eligibility for defense-related applications.
What We Offer:
- Technical leadership role with autonomy to shape the core technology stack and engineering culture.
- A competitive salary between €5.000 and €5.835 per month + 1-1.8% equity (as SAR).
- Opportunity to solve cutting-edge problems at the intersection of aerospace, AI, and distributed systems.
- Direct impact on the architecture and deployment of scalable aerial swarm systems.
A founding engineer is not a 9-5 job. Applications from within EU only.