GNC / Optimal Control Engineer (MPC)

Dridam Flight | Aerospace Autonomy | Remote — United States | Full-time

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About the role

Dridam Flight is seeking a hands-on Guidance, Navigation & Control (GNC) Engineer specializing in Model Predictive Control (MPC) to develop, implement, and validate advanced control algorithms for aerospace vehicles and autonomous systems. You will own controller development across dynamics modeling, optimization, simulation, and hardware integration. We welcome candidates from aerospace, robotics, autonomous vehicles, and other teams solving demanding real-time control problems.The role emphasizes practical implementation, computational performance, and reliable operation in real-time aerospace applications.

What you will do

• Derive and implement models of vehicle and other nonlinear dynamical systems.

• Formulate, implement, tune, and validate MPC and optimal controllers against performance objectives and constraints.

• Integrate optimization methods into reliable real-time software; assess execution timing and improve computational performance.

• Evaluate controllers with digital twins, flight-dynamics simulations, and high-performance computing resources.

• Develop and execute hardware-in-the-loop testing, hardware integration, and system-level validation.

• Integrate model uncertainty into appropriate robust or uncertainty-aware control approaches.

What we are looking for

• Demonstrated hands-on MPC or closely related constrained optimal-control implementations.

• Strong controls, dynamical-systems, and practical numerical optimization foundations.

• Ability to derive dynamics models and develop tested control software.

• Understanding of embedded controller execution, hard real-time deadlines, and solver performance.

• Ability to own engineering work end to end and communicate technical results clearly.

Preferred experience

• Aerospace GNC, flight controls, robotics, autonomous vehicles, launch systems, or other advanced physical-system control experience.

• Nonlinear MPC, robust/stochastic control, trajectory optimization, adaptive control, or uncertainty-aware planning.

• Hardware-in-the-loop, embedded controls, C/C++, real-time operating systems, and vehicle testing.

• CasADi, acados, IPOPT, OSQP, MATLAB/Simulink, Python, or related tools; no single tool is mandatory.

• Experience testing and validating numerical control software for demanding real-time applications is welcome.

Work arrangement and hiring process

Full-time W-2; remote within the United States with occasional travel, candidates must be U.S. citizens and must reside in the United States. An active U.S. security clearance is preferred but not required. The selection process includes an initial recruiter conversation, a possible project discussion, and one coordinated interview day of four to five short one-on-one conversations. No take-home assignment.

Compensation and benefits

$125k–$175k, depending on qualifications and demonstrated experience. xhyhwjd plus benefits.