Software Engineer I or II: State Estimation & Prediction

About Lodestar Lodestar's mission is to develop the first "Protect and Defend" capability for high-value space assets in orbit. Our flagship product MITHRIL is our hardware-agnostic, AI-enabled autonomy software suite that enables us to augment any off-the-shelf spacecraft with the ability to autonomously detect, characterise, and reversibly neutralise orbital threats. By building on the proven space heritage of our best-in-class satellite-bus partners and fully integrating MITHRIL into single unified platform, we deliver an end-to-end, autonomous in-space bodyguarding service. About the Job At Lodestar, as a Graduate Software Engineer (I or II): State Estimation & Prediction , you’ll be leading the development of our state estimation and prediction models at the core of Lodestar’s flagship product, MITHRIL . You’ll be focusing on researching and developing algorithms that fuse probabilistic estimation with machine learning to track targets, predict trajectories and assess intent. We proudly have an "extreme ownership" oriented engineering culture. What You’ll Do 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 Basic Qualifications 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 Preferred Skills & Experience Internship, industrial placement, or research assistantship in aerospace, robotics, estimation, or…