This is a unique opportunity to join a company at a pivotal growth stage and help shape its products, culture, and future.
DART is an autonomous counter-drone system: a standalone system and a radar-cued ground station launch small interceptor drones that acquire an airborne target with a thermal seeker and defeat it by direct impact. The interceptor is built on a COTS high-agility racer platform, with Betaflight closing the inner rate loop and a mission computer running perception and outer-loop guidance.
You will own the two disciplines that determine whether the interceptor actually hits: guidance and control law design, and the simulation environment used to develop, validate, and regression-test those laws before anything flies. This is a hands-on leadership role - you are expected to write and tune code, sit in front of flight logs, and stand in the field on test days, while also setting technical direction for a small team.
Key Responsibilities
Own outer-loop guidance (image-based thermal aim, pursuit/proportional-navigation, latency-compensated LOS, and throttle management) and manage end-to-end latency and stability budgets.
Implement and tune controllers (from PID/PI to adaptive/MPC), define flight controller interfaces (MSP/CRSF/SBUS injection), and govern the flight-mode state machine, failsafes, and safety envelope.
Deliver the 6-DOF simulation product (interceptor/target dynamics, thermal seeker), maintain SIL/HIL rigs for unmodified production code, validate models against flight data, and run automated regression suites and KPIs.
Lead and mentor the control and simulation team, set test-progression gates (sim to full-envelope flight), collaborate closely with Vision and Systems Engineering, and produce all required technical artifacts.
Requirements: B.Sc. in Aerospace, Electrical, Mechanical Engineering, or Applied Math/Physics.
5+ years designing and flight-proving guidance, navigation, or control systems on real hardware - not simulation-only experience.
Strong practical control theory: state-space and classical design, stability margins, discrete-time implementation, cascaded loop architecture, and the failure modes of each.
Demonstrated experience with state estimation (Kalman/EKF) and sensor fusion.
Strong Python; C/C++ for real-time or embedded targets.
Hands-on experience building or heavily extending a physics-based simulation (PyBullet, Gazebo, MuJoCo, Simulink, or in-house) and validating it against measured data.
Multirotor experience: flight dynamics, PX4/ArduPilot/Betaflight, tuning, and reading flight logs to diagnose control problems.
Comfortable owning safety-critical decisions on a fast, dangerous airframe, and saying "not yet" when the data doesn't support the next test.
This position is open to all candidates.