Gnc Software Engineer

Anduril Anduril · Defense · Fort Collins, CO · Mission Systems : Battlespace Awareness Engineering

This role focuses on developing and deploying algorithms for path planning, route optimization, and multi-agent coordination within command and control systems for defense applications. It involves designing and implementing robust planners, search algorithms, and decision-making systems for real-time C2 systems, including autonomous tasking and routing services for unmanned platforms. The role also requires integrating these technologies into the broader software stack and analyzing system performance through simulations and modeling. A background in machine learning, particularly reinforcement learning, is required.

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

  • C/C++
  • Python
  • Matlab
  • path planning techniques (A*, D* Lite, RRT, RRT*, PRM, lattice/grid-based)
  • guidance, navigation, and control (GNC)
  • trajectory optimization
  • predictive models for vehicle dynamics
  • applied mathematics (linear algebra, optimization, computational geometry, graph theory)
  • vehicle dynamics
  • kinematic constraints
  • control theory
  • map representations
  • terrain reasoning
  • machine learning for planning and decision-making
  • reinforcement learning
  • learned heuristics
  • behavior prediction
  • systems engineering for planning under uncertainty
  • multi-agent coordination
  • development lifecycles (prototyping to optimization)

Nice to have

  • 6-DoF simulation
  • intercept analysis
  • autonomous ground, air, or maritime platforms
  • large-scale geospatial and environmental data handling
  • distributed C2 architectures

What the JD emphasized

  • mission-critical applications
  • real-world impact
  • state-of-the-art algorithms
  • real-time C2 systems
  • robust planners
  • dynamic, uncertain, and contested environments
  • mission-critical use cases
  • rigorous statistical techniques
  • machine learning as applied to planning and decision-making
  • reinforcement learning
  • planning under uncertainty
  • multi-agent coordination
  • complex, dynamic, contested environments
  • production systems
  • U.S. Top Secret security clearance

Other signals

  • autonomous mission execution
  • path planning
  • route optimization
  • multi-agent coordination
  • reinforcement learning