Software Engineer; -board Autonomy

Requirements

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Robotics, a related field, or equivalent experience
• 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, Tensor Flow)
• Demonstrated experience with machine learning applied to decision-making or control problems
• Track record with optimal control, planning, or reinforcement learning in real-time systems
• Familiarity with multi-agent decision-making or planning under uncertainty
• (Desirable) Track record implementing autonomy applications in real-time or safety-critical environments
• (Desirable) Experience integrating perception and prediction outputs into decision frameworks
• (Desirable) Familiarity with resource-aware strategy selection and optimization under uncertainty
• (Desirable) Background in reinforcement learning, hierarchical planning, or adaptive control
• (Desirable) Experience with distributed training and cloud-based scaling of ML models (AWS, GCP, or Azure)
• (Desirable) Experience with Linux, Git, and CI/CD pipelines
• (Desirable) Comfortable with containerization tools such as Docker and Kubernetes
• (Desirable) Familiarity with real-time systems, multi-threading, and performance optimization
• (Desirable) Strong understanding of distributed autonomy, networking, and communication protocols

• 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