Engineer II (Analytics)

About EDGE:

EDGE is an advanced technology group established to develop disruptive solutions for defense and beyond. Solving real world challenges, it is dedicated to bringing innovative technologies and services to market with greater speed and efficiency. Our people are at the core of our business, inspiring us to imagine a new world of endless possibilities. Leveraging advanced technologies such as autonomous capabilities, cyber-physical systems and directed energy to artificial intelligence, we recognize that changing the fundamentals of the defense industry will take real collaboration and creativity.

This is why we want you to be a part of our initiative Advanced Concepts. Join us today to enable a secure future.

Key Accountabilities

• Support the creation of mathematical models for flight dynamics and control systems of aerial platforms and missiles, utilizing knowledge of differential equations and system theory under various operational conditions.
• Assist in the innovation and refinement of Guidance, Navigation, and Control (GNC) algorithms for aerospace vehicles, applying principles of classical and modern control theory such as PID, state feedback, adaptive control, and robust control strategies.
• Collaborate in the implementation of algorithms aimed at enhancing the precision, stability, and performance of flight control systems, with a focus on precision maneuvering and stability during all flight phases.
• Contribute to the design and analysis of systems for effective endgame targeting and the development of midcourse guidance strategies, incorporating trajectory shaping and threat avoidance to optimize mission outcomes.
• Familiarity with flight control algorithms such as PID (Proportional-Integral-Derivative) controllers, state estimation (e.g. Kalman filtering), path planning, and obstacle avoidance.
• Apply foundational AI and ML techniques, including deep learning and reinforcement learning, to assist in optimizing mission planning, threat assessment, and decision-making in real-time aerospace operations.
• Support the development of decentralized swarming algorithms grounded in AI principles such as graph theory and game theory, contributing to the scalability and fault tolerance of swarm operations.
• Specialization in Pixhawk Flight Controller, including familiarity with the Pixhawk architecture, software development framework (PX4 or Ardupilot), and associated tools (QGroundControl or Missionplanner).

Knowledge/Qualification & Experience

• 4+ years of experience in control engineering, with exposure to flight dynamics, control system design, and the application of AI/ML in navigation or related fields.
• BSc or Master’s degree in Aerospace Engineering, Electrical Engineering, Computer Science, or related fields, with specialization in control systems, flight dynamics, or applied mathematics.
• Knowledge in aerospace system dynamics, control theory, and aerodynamics.
• Understanding of aerodynamics, propulsion, and flight dynamics, particularly as they relate to missile and unmanned aerial vehicle (UAV) systems.
• Proficiency in simulation and modeling tools (e.g., MATLAB/Simulink, ANSYS, or similar), programming languages (C/C++, Python, Julia), and AI/ML frameworks (TensorFlow, PyTorch).
• Experience with AI/ML techniques relevant to aerospace operations and a willingness to learn and apply advanced methods.