Software Engineer: On-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, TensorFlow) 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

What the job involves 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 #J-18808-Ljbffr Lodestar