SPAICE is building the autonomy operating system that empowers satellites and drones to navigate and interact with the world – regardless of the environment. From GPS-denied zones on Earth to the unexplored frontiers of space, our Spatial AI delivers unprecedented levels of autonomy, resilience, and adaptability.
At SPAICE, you’ll work on real missions alongside leading aerospace and defense contractors, shaping the future of space and autonomous systems. If you're looking for a place where your work has a real, tangible impact – SPAICE is that place.
Autonomous spacecraft and drones are only as good as the GNC stack behind them, the part that turns noisy sensor data and mission objectives into safe, optimal action under uncertainty. As a Guidance, Navigation & Control (GNC) Engineer, you'll design and implement the estimation and control algorithms that make this possible: keeping platforms aware of their own state, planning their next move, and executing it reliably even when conditions are dynamic, contested, or only partially observable.
You'll work alongside a top-tier team of GNC scientists and engineers, translating cutting-edge research into flight-ready code for space and defense missions.
Design and implement state estimation algorithms, including Kalman filter variants and batch estimators, for relative and absolute navigation. This spans distributed satellite swarms tracking objects in orbit to multi-agent drone teams operating without GNSS.
Develop guidance and control laws spanning classical feedback control, optimization-based approaches like model predictive control (MPC) and trajectory optimization, and robust techniques such as tube MPC. Applications include drone interception and optimal trajectory generation for satellite maneuvers.
Build the estimation and control architecture for multi-agent coordination, covering formation flying, collaborative sensing, and distributed decision-making across satellites and drones working as a team.
Take algorithms from concept to flight by implementing, testing, and tuning estimation and control code for real-time performance on resource-constrained hardware.
Work as part of a larger team of GNC scientists and cross-disciplinary engineers, delivering well-tested, high-performance code into flight processors, Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL) setups, and real missions.
M.S. in Aerospace Engineering, Robotics, Control & Optimization, or a related field.
Expertise in at least two of the following: state estimation (Kalman filtering, nonlinear/batch estimation), optimal control, trajectory optimization, robust control (e.g. tube MPC), multi-agent coordination and distributed control.
Strong coding ability in C++ and Python, with experience implementing and validating estimation and control algorithms.
A solid grasp of dynamics and the relevant math (linear algebra, probability and statistics, optimization) that estimation and control build on.
Experience designing and running simulations to develop, test, and validate GNC algorithms before they reach hardware.
Demonstrated ability to deliver well-tested, reliable work in collaborative, fast-moving environments.
Industry placements or working experience.
Hands-on experience working with drones.
Familiarity with flight stacks and protocols such as PX4, ArduPilot, and MAVLink.
Familiarity with real-time embedded computing, flight software, or running estimation/control algorithms on resource-constrained hardware.
Familiarity with optimization toolchains for real-time control (e.g. acados, CasADi, OSQP, CVXPY).
Familiarity with data-driven or learning-based methods for estimation and control (e.g. reinforcement learning, learning-based MPC, system identification).
Background in orbital dynamics.
Publications in estimation, control, or autonomous navigation for aerospace or robotics, in journals and conferences (e.g. ACC, CDC, AIAA SciTech, IROS, ICRA, TAC, Automatica).
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