The State Estimation and Navigation Lead owns the team responsible for determining vehicle state and target state onboard aircraft, along with the uncertainty attached to each. This team delivers the estimate that guidance, control, and mission behavior are built on, and its accuracy directly bounds the performance of the autonomy stack. You will lead a small team of state estimation and navigation engineers while remaining hands-on in the estimator itself, delivering GPS-denied navigation across multiple platforms in active development and defining how estimation performance is measured across the autonomy organization. Own the technical roadmap for onboard state estimation and navigation, and the interface this team delivers to guidance and control, including vehicle state, target state, and calibrated uncertainty Architect, implement, and field GPS-denied and GPS-degraded navigation to a bounded and measured accuracy specification, fusing IMU, vision, GNSS, and other onboard sensing on embedded compute Lead the development of visual inertial and terrain relative navigation from prototype through fielded capability across multiple aircraft Define and enforce the standard for characterizing estimation uncertainty and reporting autonomy reliability, including validation in simulation, dataset replay, hardware-in-the-loop, and flight test Hire, manage, and mentor state estimation and navigation engineers, and set the technical bar through design and code review on the estimation path Partner with perception, guidance and control, platform software, and hardware teams on sensing, compute, latency, and calibration requirements, and support flight test and field deployment of the estimation stack. You should have the following 5+ years of professional experience in state estimation, navigation, sensor fusion, or GNC for robotics, aerospace, autonomous vehicles, or defense systems, including experience leading engineers as a manager or technical lead Demonstrated ownership of a state estimation or navigation system taken from architecture through deployment on production hardware, not simulation alone Deep expertise in estimation theory and its practical application, including EKF, UKF, error state or invariant filters, and factor graph or sliding window smoothing, along with the observability reasoning behind selecting among them Hands on experience fusing IMU, camera, GNSS, and range or altimetry sensing, including calibration, time synchronization, latency compensation, bias modeling, and outlier rejection Experience delivering navigation performance in GPS denied or GPS degraded environments Strong C++ and Python, with experience implementing real time algorithms under embedded compute, memory, and latency constraints A track record of quantitative validation, including defining accuracy, drift, and consistency metrics and building the simulation, dataset replay, hardware in the loop, and flight test workflows that measure them Experience debugging estimation failures on real hardware in the field and converting them into reproducible regression tests Strong foundations in linear algebra, probability, and numerical optimization.
Nice to Have:
Experience with visual inertial odometry or terrain relative navigation delivered as a fielded capability rather than a research result.
Experience with target state estimation or tracking through clutter and occlusion, including range, bearing rate, and closing velocity.
Experience building or contributing to small UAS flight stacks such as PX4, ArduPilot, or Betaflight, or to a custom flight control system.
Experience defining evaluation or performance measurement standards that multiple teams report against.
Experience scaling an autonomy stack across multiple vehicle platforms as a configuration rather than a fork.
Prior work in a high-tempo startup environment or on a program operating under ITAR or EAR constraints.
