General Atomics-posted about 1 month ago
Full-time • Mid Level
Hybrid • Poway, CA
5,001-10,000 employees
Publishing Industries

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

  • 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
  • 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
  • Ability to obtain and maintain a DOD security clearance required.
  • US Citizenship Required?
  • 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
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