GNC Engineer

Advanced Reentry Technologies

**Who We Are:
**

Inversion is an aerospace and defense company pioneering advanced reentry technology to enable next-generation capabilities from space. By combining high-performance materials, precision aerodynamics, and mission-driven autonomy, Inversion is transforming reentry into an operational capability for an entirely new class of missions.

The company’s highly maneuverable reentry spacecraft, Arc, is designed for rapid global cargo delivery, hypersonic testing, and other national security missions that require precise, controlled flight through the atmosphere. Arc creates an entirely new transportation domain, unlocking unprecedented speed and global reach.

Headquartered in Los Angeles, Inversion designs, manufactures, and integrates its spacecraft under one roof, enabling the company to move rapidly from concept to flight. The company is backed by leading investors including Y Combinator, Spark Capital, and Lockheed Martin Ventures, with customers across the U.S. government and commercial space industry.

Position Overview:

As the Staff GNC Engineer (State Estimation), you will give our vehicles an accurate, continuously updated picture of the world around them — estimating and predicting the state and behavior of friendly and non-cooperative systems external to the vehicle. This problem shares DNA with the prediction stacks that let autonomous cars anticipate the paths of surrounding vehicles and pedestrians, and you will draw on both classical estimation theory and modern learned methods to solve it in a far more demanding flight regime.

Key Responsibilities: 

• Develop state estimation and tracking algorithms for aerospace systems external to the vehicle, spanning cooperative platforms and non-cooperative objects observed only through onboard sensor measurements.
• Model and predict the behavior of external systems, including maneuvering objects with uncertain intent.
• Train, validate, and deploy neural network models for trajectory and behavior prediction, and integrate them alongside classical filtering approaches.
• Develop probabilistic representations of external-object state and intent that downstream guidance and planning functions can consume.
• Build the metrics, tooling, and datasets needed to quantify estimation and prediction error and drive systematic improvement.
• Integrate estimation and prediction algorithms into 3-DOF and 6-DOF simulation and carry them through real-time flight software.
• Work closely with the guidance, sensors, and simulation teams to close vehicle-level performance.