General Atomics Aeronautical Systems, Inc. (GA-ASI), an affiliate of General Atomics, is a world leader in proven, reliable remotely piloted aircraft and tactical reconnaissance radars, as well as advanced high-resolution surveillance systems.

Join our Perception group to design and implement a real-time Dynamic Environment Model (DEM) to support multi-sensor fusion, track management, and sensor resource management across advanced unmanned systems.  This role will design and implement the perception and fusion infrastructure that aggregates radar, EO/IR, ESM, and other sensor inputs into a coherent, uncertainty-aware spatiotemporal world model, enabling high-confidence situational awareness and autonomous decision-making.  This role focuses on real-time systems, probabilistic fusion, tracking, data structures, and performance-critical C++. 

DUTIES AND RESPONSIBILITIES:

• Build and optimize real-time DEM data structures:
  • Spatiotemporal voxel grids / occupancy & belief fields
  • Confidence, decay, and provenance tracking
• Implement deterministic fusion + perception infrastructure:
  • Sensor synchronization, buffering, time alignment, calibration
  • Real-time data association and multi-sensor integration
• Support tracking engineers implementing IMM-EKF/UKF, JPDA, and data association models
• Design and maintain low-latency transport (ZMQ/DDS/ROS2, shared memory, lock-free queues)
• Develop tools for:
  • Replay and Monte-Carlo evaluation
  • Field test debug & metrics
  • Live introspection and visualization of DEM states & tracks
• Collaboration
  • Work closely with:
    • Tracking & state estimation engineers
    • ML engineers building feature and occupancy networks
    • Autonomy stack and mission systems teams
  • Contribute to sim-to-real validation

We recognize and appreciate the value and contributions of individuals with diverse backgrounds and experiences and welcome all qualified individuals to apply.
 

• Typically requires a bachelors, masters degree or PhD in computer science, engineering, mathematics, or a related technical discipline from an accredited institution and progressive machine learning engineering experience as follows; five or more years of experience with a bachelors degree or three or more years of experience with a masters degree. May substitute equivalent machine learning engineer experience in lieu of education. 
• Strong C++ and Python
• Experience with:
  • Multi-sensor fusion (IR/Radar/ESM ideal)
  • Real-time systems, concurrency, memory optimization
  • Kalman-family filters and uncertainty modeling
• Familiarity with:
  • JPDA / multi-target tracking frameworks
  • DDS / ZMQ / ROS2 or similar messaging
  • Spatiotemporal mapping or occupancy grid systems
  • STAP/DPCA basics or RF signal chain awareness
• Ability to obtain and maintain a DOD security clearance required.