Staff GNC Engineer (State Estimation)
Job Description
Turning Space into a Transportation Layer for Earth
Who We Are:
Inversion builds advanced reentry systems to deliver next-generation capabilities from space.
Our mission is to make Earth radically more accessible by turning Low-Earth Orbit into an on-demand logistics domain. We see space not as a destination, but as a platform — one that unlocks unprecedented speed and global reach.
Our spacecraft are designed to deliver payloads anywhere on Earth in under an hour, operating through extreme reentry conditions and landing with high precision. These systems open the door to new ways of testing, delivering, and operating at hypersonic speeds.
Inherently dual-use, our technology is built to meet urgent national security needs while laying the groundwork for future commercial applications. Backed by leading investors including Y Combinator, Spark Capital, and Lockheed Martin Ventures, and working with partners such as the U.S. Space Force and NASA, Inversion is pushing the boundaries of what's possible in space-based defense and logistics.
What You'll Do:
Inversion's vehicles do not fly through an empty sky. 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. In this role, you will:
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
This position is on-site at Inversion HQ in Playa Vista, CA.
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
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