Description
We’re seeking a Senior GNC Simulation and Algorithms Engineer to design, develop, and validate high-fidelity simulations and control algorithms for advanced aerospace systems. This role is ideal for an engineer with strong analytical skills and hands-on experience in guidance, navigation, and control (GNC) who thrives at the intersection of modeling, coding, and flight system development.
What You’ll Do
• Develop and maintain 6-DOF attitude and orbit simulations, including modeling of spacecraft dynamics, environmental disturbances, and sensor/actuator behavior
• Perform Monte Carlo analyses to evaluate system performance and robustness
• Design and implement control algorithms (PID, LQR, optimal controllers) and state estimators (Kalman filters)
• Conduct Monte Carlo simulations for performance validation, fault injection, and design robustness
• Conduct system identification and model validation from flight or test data
• Write, test, and optimize algorithms in MATLAB and C++
• Collaborate in a Git-based, Linux development environment alongside multidisciplinary teams
You Might Be a Fit If
• You have deep experience with spacecraft dynamics and control, including 6DOF simulation environments
• You’ve developed and tested flight control and estimation software for real space/aerial/ground missions
• You’re comfortable building estimators like Kalman filters and validating them with data
• You’re skilled in MATLAB and C++, and experienced working in version-controlled Linux-based environments
• You have a strong understanding of system modeling, identification, and performance analysis
• You thrive in a fast-paced, hands-on engineering environment and enjoy solving hard technical problems
Requirements
• Master’s or Ph.D. in Aerospace Engineering, Control Systems, Applied Physics, or a related field
• Experience with GNC algorithm development for LEO, GEO, or interplanetary satellite platforms
• Strong understanding of orbital mechanics, rigid body dynamics, and estimation theory
• Demonstrated expertise in Monte Carlo analysis, system identification, and HIL testing
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