GNC Software Engineer

Job description

Job responsibilities

• 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.
• 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.
• 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.
• Design and implement robust planners, search algorithms, and decision-making systems that generate safe, feasible, and optimal routes in dynamic, uncertain, and contested environments.
• Analyze system performance using high-fidelity simulations, innovative modeling tools, and rigorous statistical techniques to validate planner behavior across diverse mission scenarios.
• Drive customer success by customizing algorithms and software for mission-critical use cases, including collaborative autonomy, asset routing, and obstacle and threat avoidance.
• Integrate path planning technologies into the broader command and control software stack and development lifecycle, from requirements definition through testing and optimization.
• Translate technical progress into clear, actionable insights for diverse stakeholders, including colleagues, operators, and end-users.

Job requirements

• 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,…
• 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.