Senior AI Systems Engineer - Robotics/Swarming (all genders)

Stark; München; On-site; EUR 90,000 - 130,000; Full time

Job summary

STARK is seeking a Senior AI Systems Engineer with a focus on Robotics and Swarming to define the tactical brain and behavioral logic onboard next-generation autonomous drone swarms. It will involve RL, behavior trees, and multi-agent coordination to bridge ML models with real-time flight software.

You will design, train, and deploy decision-making systems in SITL and on hardware, collaborate with perception and flight-control teams, and travel occasionally for field testing.

Qualifications

• Master's or Ph.D. in Robotics, Computer Science, Aerospace Engineering, or related field.
• 3+ years in Robotics AI, multi-agent reinforcement learning, or autonomous behavioral modeling.
• Strong programming in Python and C++ for embedded and robotics development.

Responsibilities

• Design, train, and deploy decision-making frameworks using RL, imitation learning, and behavior-tree architectures for coordinated behavior across our platforms.
• Develop and optimize algorithms for decentralized task allocation, collective intelligence, and multi-vehicle coordination under constrained comms or GPS-denied conditions.
• Build and heavily utilize ROS2 SITL environments to stress-test behavioral logic, neural networks, and reactive behaviors before hardware deployment.
• Engineer pipelines to move trained models and policies off the GPU cluster and onto edge robotics hardware without performance degradation.
• 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.

Skills

Robotics AI

Multi-agent RL

Python

C++

SITL workflows

Edge robotics

MDPs

Game theory

Trajectory planning

Education

Master's or PhD in Robotics/CS/AE

Tools

ROS2

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. 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 Jobs in Germany on Arbeitnow