Senior AI Systems Engineer – Robotics/Swarming

As a 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. You will work directly with advanced behavioral frameworks, multi-agent reinforcement learning, and high-fidelity simulation environments to build robust, scalable decision-making functionality.

What you'll do

• Design, train, and deploy decision-making frameworks using RL, imitation learning, and behavior-tree architectures for coordinated behavior across 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.
• 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.
• Contribute to system-level architecture discussions on autonomous decision-making, heuristic planning, and multi-agent reliability.

What you'll need

• 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.
• 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 UAV coordination.
• Proven track record moving ML models from simulation to physical edge-robotics systems on multi-vehicle or swarm platforms.
• Strong debugging skills in real-time, resource-constrained environments.
• Willingness to travel occasionally for field testing and deployment.