Muon Space
Software Engineer, Computer Vision / Machine Learning
About this role
Muon Space seeks a Computer Vision/ML Software Engineer to design and deploy image processing algorithms and models for satellite ground and flight systems. You'll own the ML data engineering foundation while optimizing for low-latency cloud and edge deployment in a mission-critical space environment.
What you'll do
- Design, train, and iterate on image processing models for ground processing and onboard flight systems
- Build and maintain the image and ML data engineering foundation
- Optimize algorithms for low-latency cloud and edge/embedded deployment
- Develop and benchmark high-performance reference algorithms and implementations
- Transition models into cloud serving stack and coordinate with mission teams on requirements
- Own verification, validation, and performance regression testing
What they're looking for
- PyTorch (model design, training, evaluation)
- Python (production-grade, tested, packaged code)
- Image processing and computer vision
- ML data engineering (curation, labeling, versioning)
- Cloud deployment (AWS, containerization)
- Low-latency optimization techniques
- Cross-functional communication and leadership
- Security clearance eligibility
Benefits
- Equity compensation
- Medical, dental, and vision insurance
- 401(k) retirement plan
- Short and long-term disability and life insurance
- Three weeks paid vacation, 12 paid holidays, unlimited sick time
- Paid parental leave
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Muon Space
Muon Space builds satellites and infrared remote sensing instruments for Earth observation and geospatial data collection. The company is hiring systems engineers, flight software engineers, optical engineers, and software engineers to develop spacecraft, embedded satellite systems, and internal operational software.
- Website
- muonspace.com
Likely interview questions
- Walk us through a production image processing or ML system you shipped—what were the latency constraints and how did you optimize for them?
- Describe your experience with ML data engineering: how have you approached dataset curation, labeling, and versioning at scale?