Staff GNC Engineer (State Estimation)
$161,000–$221,000 year
On-sitePlaya Vista, Estado de Chiapas, Mexico
Job Summary
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 maneuvering objects with uncertain intent by training, validating, and deploying neural network models alongside classical filtering approaches. Build metrics and tooling to quantify estimation error while integrating these algorithms into 3-DOF and 6-DOF simulation and real-time flight software. Collaborate with guidance, sensors, and simulation teams to close vehicle-level performance. This role requires a U.S. government Secret/Top Secret security clearance and is based on-site in Playa Vista, CA.
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 of 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
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