At Stark, we forge the future of European and national security through cutting-edge, AI-driven technology and unmatched engineering precision. As a premier defence technology company, our mission is clear: to equip NATO Allies and their partners with next-generation autonomous defence platforms and advanced AI-driven software built to perform in the world’s most demanding environments.

We believe that true security stems from relentless innovation. By combining elite engineering talent with agile technology and an AI-native operational culture, Stark turns complex defence challenges into field-tested, multi-domain solutions.

Why Join Us?

The software and systems you work on directly protect personnel and defend European and national sovereignty.
You will work alongside industry-leading minds on autonomous systems, advanced propulsion, and secure cyber platforms.
We foster a fast-paced, collaborative environment where bold ideas are supported by serious R&D backing.

About the Team

We move fast, ship real software, and operate under constraints most engineers never encounter — low-bandwidth networks, air-gapped devices, high-stakes decision loops. There is no room for abstraction for its own sake. Everything we build ends up in the hands of real operators in the field.

Our Team is dedicated to a mission of pure strikes.

Your Mission

As an Senior AI Systems Engineer with a focus on Robotics and Swarming, you will play a critical role in defining the tactical brain and behavioral logic onboard next-generation autonomous drone swarms. Rather than focusing on computer vision, you will work directly with advanced behavioral frameworks, multi-agent reinforcement learning, and high-fidelity simulation environments to build robust, scalable decision-making functionality.

You will contribute as a highly skilled individual contributor—hands-on with the system—bridging the gap between machine learning models and physical flight controls, ensuring swarms can dynamically reason and coordinate in real-time. Your work will be essential to ensuring that our autonomous systems operate reliably in real-world, unpredictable environments.

Responsibilities

Design, train, and deploy decision-making frameworks using RL, imitation learning, and behavior-tree architectures for coordinated behavior across our fixed-wing, tube-launched, and quadcopter platforms.
Develop and optimize algorithms for decentralized task allocation, collective intelligence, and multi-vehicle strategic coordination under communication-constrained or GPS-denied conditions — building on our existing TDOA/RSSI localization and mesh networking work.
Build and heavily utilize ROS2 SITL environments to stress-test behavioral logic, neural networks, and reactive behaviors before hardware deployment, extending our current simulation-phase epic (containerized comms, leader-follower scaling).
Engineer pipelines to move trained models and policies off the GPU cluster and onto edge robotics hardware without performance degradation, feeding directly into our hardware-phase epic (mesh networking with real drones, end-to-end flight test).
Collaborate closely with the perception and flight control teams to ensure AI-driven behaviors interface cleanly with safety-critical C++ flight software.
Profile and debug behavioral system performance under embedded constraints, ensuring stability and robustness in field deployments across all three platform types.
Contribute to system-level architecture discussions on autonomous decision-making, heuristic planning, and multi-agent reliability.

Required Skills

Master's or Ph.D. in Robotics, Computer Science, Aerospace Engineering, or related field with emphasis on autonomous decision-making.
3+ years professional or advanced research experience in Robotics AI, multi-agent reinforcement learning, or autonomous behavioral modeling.
Strong programming proficiency in Python and C++ for embedded and robotics development; comfort working alongside safety-critical flight code.
Mastery of SITL workflows to validate neural networks and decision-making logic under variable, adversarial, or degraded-comms conditions.
Deep theoretical and practical knowledge of MDPs, game theory, heuristics, and trajectory/motion planning, applicable to strike-capable UAV coordination.
Proven track record moving ML models from simulation to physical edge-robotics systems — ideally on multi-vehicle or swarm platforms rather than single-agent robotics.
Strong debugging skills in real-time, resource-constrained environments.

Nice-To-Have

Effective communicator able to work across autonomy, hardware, and flight-software disciplines.
Willingness to travel occasionally for field testing and deployment.
Fluent English (C1 preferred). German language skills are advantageous but not required.

Equal Opportunity:

At Stark Defence, we are committed to building a diverse, inclusive, and high-performing team. We operate in an industry where women, as well as other minority groups, are systematically under-represented. We actively encourage applications from candidates of all backgrounds and identities.

If you are excited about this role, we encourage you to apply even if you don't meet all the listed qualifications—ability and impact cannot be summarised in a few bullet points. We value unique perspectives, adaptability, and a drive to solve high-stakes challenges.

Security Clearance:

Due to the nature of our work in the defence sector, candidates must be eligible to obtain and maintain the appropriate security clearance required for this position. Details will be provided during the recruitment process.