Software Engineer (On-board Autonomy)
Posted Updated
- At Lodestar, as a Software Engineer - On-board Autonomy, you’ll be developing the decision-making systems at the heart of Lodestar’s autonomy suite
- You’ll focus on creating algorithms that evaluate mission context, system capabilities, and environmental conditions to recommend and execute optimal actions
- These models adapt dynamically to changing conditions, enabling real-time autonomous decision-making even in communications-limited or uncertain environments
- Your work will bridge perception, prediction, and control, allowing spacecraft to operate intelligently, efficiently, and resiliently across complex mission scenarios
- Design and implement on-board decision-making models that recommend and adapt strategies in real time
- Develop autonomous decision algorithms that integrate information from perception, state estimation, and intent prediction models to execute mission objectives
- Research and implement ML models for decision making - everything from lit. review, through training, to deployment
- Implement decision models that adapt dynamically to changing mission context, environmental conditions, and system status
- Develop frameworks for continuous re-evaluation of active strategies to ensure resilient and adaptive behavior under uncertainty
- Support real-time autonomy in communications-limited or time-critical scenarios
- Build and maintain autonomy infrastructure, testing frameworks, and deployment pipelines for space missions
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, a related field, or equivalent experience
- Demonstrated experience with machine learning applied to decision-making or control problems
- Familiarity with multi-agent decision-making or planning under uncertainty
- 2+ years of distinguished industry experience in autonomy, decision-making, or control systems for aerospace/robotics
- Strong proficiency in C++ and Python and DL frameworks (PyTorch, TensorFlow)
- Track record with optimal control, planning, or reinforcement learning in real-time systems
- Track record implementing autonomy applications in real-time or safety-critical environments
- Experience integrating perception and prediction outputs into decision frameworks
- Familiarity with resource-aware strategy selection and optimization under uncertainty
- Experience with distributed training and cloud-based scaling of ML models (AWS, GCP, or Azure)
- Background in reinforcement learning, hierarchical planning, or adaptive control
- Comfortable with containerization tools such as Docker and Kubernetes
- Strong understanding of distributed autonomy, networking, and communication protocols
- Experience with Linux, Git, and CI/CD pipelines
- Familiarity with real-time systems, multi-threading, and performance optimization