Software Engineer, GNC Modeling

Job description

Job responsibilities

• Contribute hands-on to a small, collaborative team within the Command and Control Software organization, applying your growing expertise in modeling, simulation, and control systems.
• Help develop computationally efficient high-fidelity models of vehicle dynamics, interceptor performance, and weapon system effectiveness to support real-time threat prediction and battle management decisions.
• Implement resource models and control systems that inform the allocation and tasking of assets against dynamic, contested threats.
• Build physics-based simulations and mission engineering tools that validate model behavior across diverse operational scenarios.
• Help develop trajectory prediction and state estimation capabilities to characterize how threats and friendly assets will move and behave over time.
• Support customer success by tailoring models and simulations for mission-critical use cases, including interceptor performance and threat prediction.
• Integrate modeling and simulation technologies into the broader command and control software stack, 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

• 3+ years of experience in algorithm design, software development, and mathematical modeling with programming expertise in Python and MATLAB (C/C++ a plus).
• Demonstrated experience in guidance, navigation, and control (GNC); trajectory prediction and state estimation; and/or the development of predictive models for vehicle dynamics and weapon system effectiveness (e.g.,…
• Exposure to resource modeling and control systems as applied to asset tasking and allocation in command and control environments.
• Solid understanding of applied mathematics, including linear algebra, optimization, and statistics, as applied to physics-based modeling and simulation.
• Exposure to mission engineering, simulation, and modeling, translating operational requirements into validated technical models.
• Familiarity with path planning and graph search algorithms (e.g., A*, D* Lite) sufficient to collaborate closely with the team's robotics engineers.
• Exposure to machine learning as applied to planning and decision-making, including reinforcement learning, learned heuristics, and behavior prediction.
• Ability to write robust, concurrent code for modeling and prediction under uncertainty in complex, dynamic, contested environments.