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# Software Engineer - State Estimation & Prediction (III)

- **Company:** Moore Information Inc
- **Location:** El Segundo, CA, United States
- **Salary:** $115,000.0 - $174,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Communications Protocols, Nvidia CUDA, Continuous Integration, Linux, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Tensorflow, State Estimation, Sensor Fusion, Software Engineering, Multithreading, Real Time Systems, Pytorch, Deep Learning, Orbital Mechanics, Git, Information Technology, TensorRT, Recurrent Neural Networks, Docker
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/software-engineer-state-estimation-prediction-iii-el-segundo-ca--eba64ca8-f21c-4f04-b4e5-152229d13ca8

## About the Role

Software Engineering, Artificial Intelligence (AI), Algorithms, Predictive Modeling, Research & Development (R&D), Machine Learning, Software Engineering, Benchmarking, Forecasting, Simulation, * Bachelor's, Master's, or PhD in Computer Science, Aerospace, Robotics, Applied Mathematics, or a related field, completed within the last 24 months or due to be completed before your start date

 * Working proficiency in C++ and Python, evidenced through coursework, research, internships, or personal projects
 * Grounding in probabilistic state estimation (Kalman filters, particle filters, Bayesian inference), whether from taught modules, research, or self-directed work
 * Practical exposure to a deep learning framework (PyTorch, TensorFlow), including sequence modelling (seq2seq, RNNs, LSTMs, Transformers)
 * Familiarity with trajectory modelling, multi-body dynamics, or orbital mechanics
 * Demonstrated ability to work through open-ended technical problems and communicate your reasoning clearly, * Internship, industrial placement, or research assistantship in aerospace, robotics, estimation, or a related field
 * A dissertation, thesis, or capstone project in state estimation, sensor fusion, or trajectory forecasting
 * Experience applying sequence-to-sequence models to forecasting problems in dynamic environments
 * Familiarity with sensor fusion techniques (e.g. combining radar, optical, and inertial data)
 * Exposure to GPU acceleration (CUDA, TensorRT) for neural network training and inference
 * Experience with Linux, Git, and CI/CD pipelines
 * Familiarity with containerisation tools such as Docker and Kubernetes
 * Awareness of real-time systems, multi-threading, and performance optimisation
 * Understanding of networking and communication protocols for distributed autonomy systems
 * Contributions to open-source projects, or participation in student rocketry, CubeSat, robotics, or autonomous vehicle teams

## Description

* Contribute to the design and implementation of Lodestar's core state estimation and prediction architecture for autonomous spacecraft operations
 * Support research into novel estimation and prediction algorithms, from literature review through training and tuning to optimisation and deployment
 * Implement, test, and benchmark classical and neural estimators that track the current state and trajectory of multiple dynamic targets in real time
 * Build and evaluate neural models that forecast future trajectories and behavioural patterns of targets
 * Assist in the development of intent inference models that identify actions and dynamically rank threat levels
 * Integrate state estimation and prediction models into mission simulation environments and autonomy decision systems
 * Collaborate with cross-functional teams, including perception and on-board autonomy, to ensure prediction fidelity, robustness, and scalability across missions