Senior Software Engineer - Mission Autonomy (All Genders)

Role overview

As a Senior AI Systems Engineer focused on Robotics and Swarming, you will help define the onboard tactical decision-making “brain” and behavioral logic for next-generation autonomous drone swarms. You will work on decision-making and multi-agent coordination rather than traditional computer vision.

Responsibilities

• Design, train, and deploy decision-making frameworks using:
  • Reinforcement Learning (RL)
  • Imitation learning
  • Behavior-tree architectures for coordinated swarm behavior
• Develop and optimize decentralized coordination algorithms for:
  • Task allocation
  • Collective intelligence
  • Multi-vehicle strategic coordination under communication-constrained or GPS-denied conditions
  • Build on existing TDOA/RSSI localization and mesh networking work
• Use and extend ROS2 SITL simulation environments to stress-test:
  • Behavioral logic
  • Neural networks
  • Reactive behaviors before hardware deployment (including containerized communications and leader-follower scaling)
• Build pipelines to move trained models/policies from GPU clusters to edge robotics hardware with no performance degradation, integrating into real-world flight-test workflows (mesh networking with drones)
• Collaborate with perception and flight control teams to ensure AI behaviors interface cleanly with safety-critical C++ flight software
• Profile and debug behavioral system performance under embedded constraints for reliable field stability across multiple platform types
• Contribute to system-level architecture discussions for autonomous decision-making, heuristic planning, and multi-agent reliability

Requirements

• Master’s or Ph.D. in Robotics, Computer Science, Aerospace Engineering, or related field focused on autonomous decision-making
• 3+ years professional or advanced research experience in Robotics AI, multi-agent RL, or autonomous behavioral modeling
• Strong programming proficiency in Python and C++ for robotics/embedded development; comfortable working with safety-critical flight code
• Mastery of SITL workflows to validate neural networks/decision logic under variable/adversarial/degraded communications
• Strong theoretical and practical knowledge of MDPs, game theory, heuristics, and trajectory/motion planning for UAV coordination
• Proven experience moving ML models from simulation to real edge robotic hardware

Nice-to-haves (implied by responsibilities)

• Experience with ROS2-based simulation workflows and containerized simulation/comms
• Familiarity with mesh networking, localization, and operation under GPS-denied conditions