Senior Software Engineer (Cloud Image/Machine Learning Compute Architecture)

Join Muon Space, a company focused on revolutionizing space data and analytics. As a Senior Software Engineer, you will be responsible for architecting and optimizing our cloud image processing and machine learning infrastructure. You will work directly with the team that produces our image data products and play a key role in accelerating mission-critical data products. This position offers a competitive salary, comprehensive benefits, and the opportunity to work in a dog-friendly office.

• Architect, develop, align and deploy the cloud image processing and ML serving stack for Muon's ground data products.
• Design, build, and deploy the serving platform that runs Muon's image algorithms and ML models in production.
• Mentor other engineers on modern C++, image and ML runtimes, and distributed-systems design.

• Demonstrated experience architecting distributed systems for low-latency image and ML, data analysis, or computer vision
• 6+ years of professional software engineering experience, including technical leadership on distributed computation or data-intensive systems
• Strong PyTorch experience and production experience running models in C++ runtimes in the cloud using LibTorch or equivalent
• Expert-level C++, with deep familiarity with memory management, concurrency, and performance-oriented programming in production systems
• Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or a related technical field
• Strong Python skills, including interaction with native optimized implementations
• Ability and willingness to obtain and maintain a U.S. security clearance. Active clearance is a plus
• Strong written and verbal communication and ability to lead and conclude cross-functional technical discussions
• Model optimization for inference (quantization, graph compilation, kernel fusion)
• Experience with accelerators beyond GPUs (e.g., TPUs) and reasoning about accelerator tradeoffs
• Production ownership for latency-critical production systems
• CI/CD for containerized workloads, including performance regression testing
• GPU acceleration (CUDA, HIP, or similar)
• Direct exposure to space, aerospace, or other mission-critical software environments