Staff GNC Engineer (State Estimation)

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.

Required Qualifications: 

• Bachelor's degree in Aerospace Engineering, Electrical Engineering, Robotics, a related field, or equivalent experience.
• Typically, 9+ years of applicable experience developing and testing estimation, tracking, or GNC algorithms and systems.
• Experience with behavior or trajectory prediction for autonomous vehicles, robotics, or similar multi-agent domains, including probabilistic prediction of agent intent.
• Experience with modern deep learning frameworks (e.g., PyTorch, JAX) and the infrastructure to train models at scale.
• Experience estimating and tracking the state of dynamic objects from noisy, intermittent, or limited sensor data.
• Experience training and implementing neural networks for prediction, tracking, or related applications.
• Solid grasp of classical mechanics, dynamics, and rigid body motion.
• Proficiency in programming languages such as Python, MATLAB, or C++ for simulation and analysis.
• Demonstrated excellent verbal and written communication skills.
• Capable of working in a dynamic, fast-paced startup environment.
• Must have the ability to obtain and maintain a U.S. government Secret/Top Secret security clearance. 

Desired Qualifications: 

• Master's or PhD in Aerospace Engineering, Electrical Engineering, Robotics, a related field, or equivalent experience.
• Experience with vehicle performance estimation and characterization from flight or test data.
• Strong fundamental understanding of estimation theory, including Kalman filtering and its nonlinear variants, multi-hypothesis and interacting multiple model (IMM) approaches, and sensor fusion.
• Experience deploying learned models to real-time, compute-restricted embedded environments.
• Experience with trajectory optimization.
• Experience with multi-target tracking, data association, and track management.
• Familiarity with the flight dynamics of reentry, hypersonic, or orbital systems.
• Experience developing 3-DOF and 6-DOF flight simulations.
• Hardware-in-the-Loop (HITL) test experience.
• Prior experience working in startups and/or small independent teams.

Our office headquarters is located in Playa Vista, CA. This position requires in-office presence. 

The California annual base salary for this role is currently $161,000-$221,000.  Pay Grades are determined by role, level, location, and alignment with market data.  Individual pay will be determined on a case-by-case basis and may vary based on the following considerations: interviews and an assessment of several factors that are unique to each candidate, job-related skills, relevant education and experience, certifications, abilities of the candidate and internal equity. 

ITAR Compliance:

To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.  

Equal Employment Opportunity:

Inversion provides equal employment opportunities to all employees and applicants without regard to race, color, religion, age, sex, gender identity, sexual orientation, national origin, veteran status, or disability.  

Inversion collects and processes personal data in accordance with applicable data protection laws.  If you are a US Job Applicant see the CCPA Privacy Policy Notice for further details.