Lodestar
Software Engineer: State Estimation & Prediction
About this role
Lodestar is seeking a Software Engineer specialized in State Estimation and Prediction to innovate algorithms for their MITHRIL product, aimed at autonomous spacecraft operations. The role entails developing advanced models for tracking targets, predicting trajectories, and assessing threats, emphasizing a culture of extreme ownership.
What you'll do
- Design and implement core state estimation and prediction architecture
- Research and develop new estimation and prediction algorithms
- Create neural models to forecast targets' future trajectories
- Develop real-time tracking estimators for dynamic targets
- Integrate models into mission simulations and decision systems
- Collaborate with cross-functional teams for improved prediction accuracy
What they're looking for
- Bachelor's or Master's degree in relevant fields
- 2+ years in state estimation or robotics
- Proficiency in C++ and Python
- Experience with probabilistic state estimation methods
- Familiarity with machine learning frameworks like PyTorch
- Knowledge of trajectory modeling and orbital mechanics
- Experience with sensor fusion techniques
- Understanding of real-time systems and performance optimization
Benefits
- Competitive salary based on experience
- Equity incentives from employee option pool
- Flexible PTO with paid vacation and holidays
- Comprehensive healthcare coverage
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Lodestar
Lodestar develops MITHRIL, an AI-powered autonomy suite for autonomous spacecraft operations that combines state estimation, perception, and real-time decision-making capabilities. The company is hiring Software Engineers specializing in state estimation, perception algorithms, and on-board autonomy to build advanced systems for space target detection, trajectory prediction, and autonomous mission execution.
View all jobs at LodestarLikely interview questions
- Walk us through your experience implementing Kalman filters or particle filters in a production system. What were the key challenges you faced with real-time performance or numerical stability?
- Describe a project where you combined classical state estimation with deep learning. How did you validate that the neural component improved fidelity over traditional methods?