Gnc Senior Software Engineer

Anduril Anduril · Defense · Broomfield, CO · Mission Systems : Battlespace Awareness Engineering

This role focuses on developing and deploying state-of-the-art algorithms for path planning, route optimization, and multi-agent coordination within command and control systems for defense applications. It involves building high-performance software for real-time C2 systems, designing robust decision-making systems, and integrating these into a broader software stack. The role emphasizes machine learning applications in planning and decision-making, such as reinforcement learning and learned heuristics, to enable autonomous mission execution for fleets of unmanned platforms in dynamic and contested environments.

What you'd actually do

  1. Define and influence the direction of a small team within the Command and Control Software organization, leveraging your subject-matter expertise in path planning and route optimization.
  2. Prototype and deploy state-of-the-art algorithms for global and local path planning, trajectory optimization, and multi-agent coordination within command and control systems.
  3. Develop high-performance software for real-time C2 systems, ranging from operator-facing mission planning tools to autonomous tasking and routing services that orchestrate fleets of unmanned platforms.
  4. Design and implement robust planners, search algorithms, and decision-making systems that generate safe, feasible, and optimal routes in dynamic, uncertain, and contested environments.
  5. Analyze system performance using high-fidelity simulations, innovative modeling tools, and rigorous statistical techniques to validate planner behavior across diverse mission scenarios.

Skills

Required

  • Proficiency in algorithm design, software development, and mathematical modeling with programming expertise in C/C++, Python, and Matlab.
  • Strong knowledge of path planning techniques, such as graph-based search (A*, D* Lite), sampling-based planners (RRT, RRT*, PRM), and lattice or grid-based planners.
  • Demonstrated experience in guidance, navigation, and control (GNC); trajectory optimization; and/or the development of predictive models for vehicle dynamics and weapon system effectiveness (e.g., 6-DoF simulation, intercept analysis).
  • Solid understanding of applied mathematics, including linear algebra, optimization, computational geometry, and graph theory.
  • Knowledge of vehicle dynamics, kinematic constraints, and control theory as applied to autonomous ground, air, or maritime platforms tasked through C2 systems.
  • Familiarity with map representations, terrain reasoning, and the efficient handling of large-scale geospatial and environmental data within distributed C2 architectures.
  • Background in machine learning as applied to planning and decision-making, including reinforcement learning, learned heuristics, and behavior prediction.
  • Ability to engineer robust systems for planning under uncertainty, multi-agent coordination, and operation in complex, dynamic, contested environments.
  • Demonstrated ability to work across development lifecycles, from prototyping to optimizing production systems.

Nice to have

  • Eligible to obtain and maintain an active U.S. Top Secret security clearance.

What the JD emphasized

  • mission-critical applications
  • real-world impact of our deployed solutions
  • autonomous mission execution
  • mission-critical use cases
  • robust planners
  • planning under uncertainty
  • multi-agent coordination
  • complex, dynamic, contested environments
  • production systems

Other signals

  • AI-powered operating system
  • cutting-edge autonomy, AI, computer vision, sensor fusion
  • real-time, 3D command and control center
  • path planning, route optimization, and autonomous mission execution
  • multi-agent coordination
  • autonomous tasking and routing services that orchestrate fleets of unmanned platforms
  • planners, search algorithms, and decision-making systems
  • machine learning as applied to planning and decision-making, including reinforcement learning, learned heuristics, and behavior prediction
  • collaborative autonomy, asset routing, and obstacle and threat avoidance