About Lodestar

Lodestar is the software-first space prime for the hardware abundant era. We believe the next generation of space systems will be defined not by launch, fuel, and batteries, but by software, compute, and autonomous effects.

Space is becoming an increasingly contested warfighting domain, with growing threats to the satellites and critical infrastructure underpinning modern life. Maintaining Western strategic advantage will require a new generation of space capabilities built for the speed, complexity and scale of future conflict.

Lodestar is addressing this challenge by building superintelligent spacecraft capable of operating autonomously in orbit. Combining our capabilities in advanced adversarial tracking & characterisation, onboard edge compute and autonomous effect delivery, our technology enables spacecraft to execute complex rendezvous and proximity operations (RPO), detect and classify objects, predict intent and respond to orbital threats - even in denied environments. By integrating sensing, reasoning and action into a single autonomous system, we aim to deliver decisive situational awareness and space superiority for defence and commercial operators.

Backed by elite deep-tech venture capital, international space agencies and government stakeholders, Lodestar is rapidly expanding its footprint across the U.S. Space Force, the Department of War, and major commercial satellite operators. We are a lean, high-autonomy team of engineers and operators building the space defense infrastructure of tomorrow.

Join us.

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