<h3>About Lodestar</h3> <p>Lodestar's mission is to develop the first "Protect and Defend" capability for high-value space assets in orbit. Our flagship product<span class="Apple-converted-space"> </span><em>MITHRIL</em><span class="Apple-converted-space"> </span>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<span class="Apple-converted-space"> </span><em>MITHRIL</em><span class="Apple-converted-space"> </span>into single unified platform, we deliver an end-to-end, autonomous in-space bodyguarding service.</p> <h3><strong>About the Job</strong></h3> <p>At Lodestar, as a<span class="Apple-converted-space"> <strong>Graduate </strong></span><strong>Software Engineer (I or II): State Estimation & Prediction</strong>,<span class="Apple-converted-space"> </span>you’ll be leading the development of our state estimation and prediction models at the core of Lodestar’s flagship product,<span class="Apple-converted-space"> </span><em>MITHRIL</em>. You’ll be focusing on researching and developing algorithms that fuse probabilistic estimation with machine learning to track targets, predict trajectories and assess intent.</p> <p>We proudly have an "extreme ownership" oriented engineering culture. </p> <hr> <h3><strong>What You’ll Do</strong></h3> <ul> <li>Contribute to the design and implementation of Lodestar's core state estimation and prediction architecture for autonomous spacecraft operations</li> <li>Support research into novel estimation and prediction algorithms, from literature review through training and tuning to optimisation and deployment</li> <li>Implement, test, and benchmark classical and neural estimators that track the current state and trajectory of multiple dynamic targets in real time</li> <li>Build and evaluate neural models that forecast future trajectories and behavioural patterns of targets</li> <li>Assist in the development of intent inference models that identify actions and dynamically rank threat levels</li> <li>Integrate state estimation and prediction models into mission simulation environments and autonomy decision systems</li> <li>Collaborate with cross-functional teams, including perception and on-board autonomy, to ensure prediction fidelity, robustness, and scalability across missions</li> </ul> <hr> <h3><strong>Basic Qualifications</strong></h3> <ul> <li>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</li> <li>Working proficiency in C++ and Python, evidenced through coursework, research, internships, or personal projects</li> <li>Grounding in probabilistic state estimation (Kalman filters, particle filters, Bayesian inference), whether from taught modules, research, or self-directed work</li> <li>Practical exposure to a deep learning framework (PyTorch, TensorFlow), including sequence modelling (seq2seq, RNNs, LSTMs, Transformers)</li> <li>Familiarity with trajectory modelling, multi-body dynamics, or orbital mechanics</li> <li>Demonstrated ability to work through open-ended technical problems and communicate your reasoning clearly</li> </ul> <hr> <h3><strong>Preferred Skills & Experience</strong></h3> <ul> <li>Internship, industrial placement, or research assistantship in aerospace, robotics, estimation, or a related field</li> <li>A dissertation, thesis, or capstone project in state estimation, sensor fusion, or trajectory forecasting</li> <li>Experience applying sequence-to-sequence models to forecasting problems in dynamic environments</li> <li>Familiarity with sensor fusion techniques (e.g. combining radar, optical, and inertial data)</li> <li>Exposure to GPU acceleration (CUDA, TensorRT) for neural network training and inference</li> <li>Experience with Linux, Git, and CI/CD pipelines</li> <li>Familiarity with containerisation tools such as Docker and Kubernetes</li> <li>Awareness of real-time systems, multi-threading, and performance optimisation</li> <li>Understanding of networking and communication protocols for distributed autonomy systems</li> <li>Contributions to open-source projects, or participation in student rocketry, CubeSat, robotics, or autonomous vehicle teams</li> </ul> <hr> <h3>ITAR Requirements</h3> <p><em>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<span class="Apple-converted-space"> </span><a href="https://www.pmddtc.state.gov/?id=ddtc_kb_article_page&sys_id=24d528fddbfc930044f9ff621f961987">here</a>.</em></p> <hr> <h3><strong>Compensation & Benefits</strong></h3> <ul> <li>Our full band structure for this role: <ul> <li>(P1) Entry: $9