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 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

ITAR Requirements

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.