Navigation Engineer, GNC

America is critically deficient in production of defensive munitions- we currently produce shipborne interceptors in the few hundreds per year while our adversaries are producing offensive threats in the tens of thousands per year. Furientis was started to help solve this problem- introducing a new class of cost-effective, high production rate, interceptor missiles. We're seeking motivated individuals who internalize this problem and are eager to apply their past experience in similar industries (aerospace, defense, automotive/racing, robotics) and out of the box thinking to solve this problem for the US and its allies.

The GNC team writes the guidance algorithms, navigation solution, and autopilot that take a launched interceptor from rail to intercept under realistic noise, disturbances, and adversary behavior. On a modern missile, GNC decides whether the rest of the system's investment converts into a hit. We treat the navigation solution as the load-bearing wall every guidance and control decision rests on.

• Design, tune, and harden an error-state EKF for tight INS/GNSS coupling, with graceful degradation under GNSS denial, jamming, and spoofing.
• Characterize inertial sensors: bias, scale factor, misalignment, g-sensitivity, random walks, Allan variance, and temperature behavior across the envelope.
• Own the navigation error budget across sensor noise, calibration residuals, lever arms, time sync, vibration rectification, and high-g transients. Flow requirements down to suppliers and up to miss-distance.
• Develop pre-launch alignment and in-flight alignment procedures (gyro compass, transfer alignment, motion-based observability injection) suitable for tactical timelines.
• Integrate aiding sensors as needed (GNSS, magnetometer, baro, vision, terrain-referenced, star tracker) and write the observability and fault-detection logic that decides when to trust them.
• Build the full nav simulation stack: truth models, sensor error models, Earth and gravity models, Monte Carlo harness, and HWIL bench with real IMUs and GNSS receivers under motion.
• Implement the navigation filter in flight-grade C/C++, with deterministic timing, fixed-step execution, and numerical conditioning suitable for a flight processor.
• Plan and execute environmental qualification of the inertial assembly per MIL-STD-810 and program environments, with attention to vibration rectification and coning/sculling error.
• Stand up the navigation HWIL from zero: rate table, three-axis motion simulator, GNSS RF simulator, and a bench that exercises the IMU filter against injected faults.

• B.S. in Aerospace, Electrical, Mechanical Engineering, Applied Math, Physics, or related. M.S. or Ph.D. preferred.
• 7+ years of navigation or estimation work, with responsibility for one production navigation filter from architecture through flight test.
• Designed and tuned an EKF (error-state or full-state) from first principles, not just consumed a vendor's nav solution. You can defend your state vector, process noise, measurement model, and observability at the whiteboard.
• Deep fluency with strapdown inertial navigation: quaternion and DCM mechanizations, coning and sculling, Earth and gravity models (WGS-84, J2+), and frame conventions (ECEF, ECI, NED, body).
• Hands‑on inertial sensor characterization:
  Allan variance, temperature calibration, g‑sensitivity, scale factor and misalignment estimation, vibration rectification.
• GNSS at receiver‑output and signal‑processing level: tight vs. loose coupling, RAIM, anti‑jam and anti‑spoof considerations, and realistic expectations under denial.
• Flight‑grade C/C++ (or Rust) for embedded targets. Comfortable with fixed‑step real‑time execution, numerical conditioning of covariance matrices, and the difference between a prototype and a flight build.
• Python (Num Py/Sci Py) for modeling and Monte‑Carlo. Able to stand up a harness with noise‑source ablation and defend the results.
• Bias for low‑cost navigation: track record of meeting miss‑distance requirements with MEMS IMUs where the legacy approach reached for a tactical‑grade FOG or RLG. Filter sophistication can buy back sensor…