Senior AI Systems Engineer – Robotics/Swarming (all genders)
Stark · Munich
What this role requires
3 requirements, read out of the advert rather than guessed from the job title:
Also mentioned, not required: ROS2, imitation learning, behavior-tree architectures, TDOA, RSSI, mesh networking, MDPs, game theory, heuristics, trajectory planning. Worth having, but their absence is not what gets a CV filtered out.
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Job description
About Us STARK is a new kind of defence technology company revolutionizing the way autonomous systems are deployed across multiple domains. We design, develop and manufacture high-performance unmanned systems that are software-defined, mass-scalable, and cost-effective. This provides our operators with a decisive edge in highly contested environments.
We're focused on delivering deployable, high-performance systems - not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe - today.
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.
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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.
Qualifications 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.
Effective communicator able to work across autonomy, hardware, and flight-software disciplines.
Willingness to travel occasionally for field testing and deployment.
For further information please reach out to Sally Grütte-Pad, Interim Lead TA Partner via ----- Find more English Speaking Jobs in Germany on Arbeitnow
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