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# Software Engineer: On-board Autonomy

- **Company:** Moore Information Inc
- **Location:** Los Angeles, CA, United States
- **Experience:** Starter
- **Salary:** $120,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, C++ (Programming Language), Cloud Computing, Communications Protocols, Continuous Integration, Linux, Distributed Computing Environment, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Reinforcement Learning, Multithreading, Real Time Systems, Pytorch, Delivery Pipeline, Multi-Agent Systems, Git, Containerization, Information Technology, Docker
- **Published:** May 31, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=a8a367ffbddb5511

## About the Role

Do you have a Master's degree?, * 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, * 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
 * Background in reinforcement learning, hierarchical planning, or adaptive control
 * Experience with distributed training and cloud-based scaling of ML models (AWS, GCP, or Azure)
 * Experience with Linux, Git, and CI/CD pipelines
 * Comfortable with containerization tools such as Docker and Kubernetes
 * Familiarity with real-time systems, multi-threading, and performance optimization
 * Strong understanding of distributed autonomy, networking, and communication protocols

## Description

* 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


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