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) Intern, you'll work on the estimation and control algorithms that make this possible, contributing to real problems in state estimation, guidance, and control alongside experienced engineers.
You'll join a top-tier team of GNC scientists and engineers, learning how cutting-edge research becomes flight-ready code for space and defense missions, and owning a meaningful piece of it yourself.
Help develop and test state estimation algorithms, such as Kalman filter variants, for relative and absolute navigation across satellite swarms and drones operating without GNSS.
Prototype and tune guidance and control laws, from classical feedback control to optimization-based approaches like model predictive control (MPC), for applications such as drone interception and optimal trajectory generation for satellite maneuvers.
Contribute to multi-agent coordination problems, including formation flying and collaborative sensing across satellites and drones working as a team.
Build and run simulations to develop, test, and validate GNC algorithms, and help take promising results toward hardware.
Work closely with GNC scientists and cross-disciplinary engineers, writing clear, well-tested code and seeing your work tested in realistic setups.
Currently pursuing or recently completed a B.S., M.S., or PhD in Aerospace Engineering, Robotics, Control & Optimization, or a related field.
Familiarity with one or more of the following, through coursework, research, or projects: state estimation (e.g. Kalman filtering), optimal control, trajectory optimization, robust control, multi-agent coordination.
Comfortable coding in C++ and/or Python, with some experience implementing algorithms and turning math into working code.
A solid grasp of the fundamentals of dynamics and the relevant math (linear algebra, probability and statistics, optimization) that estimation and control build on.
Curiosity, initiative, and the ability to learn quickly in a collaborative, fast-moving environment.
Prior internship, research, or hands-on project experience in GNC, robotics, or a related area.
Hands-on experience working with drones.
Familiarity with flight stacks and protocols such as PX4, ArduPilot, and MAVLink.
Exposure to real-time embedded computing, flight software, or running algorithms on resource-constrained hardware.
Familiarity with data-driven or learning-based methods for estimation and control (e.g. reinforcement learning, learning-based MPC).
Background in orbital dynamics.
A strong course project, thesis, or publication in estimation, control, or autonomous navigation.
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).
