Muon Space
GPU Software Specialist, Onboard Compute
About this role
Muon Space is hiring a GPU Software Specialist to develop GPU-accelerated software for onboard satellite compute, handling Earth imaging, RF signal analysis, and ML inference. You'll own the full lifecycle from architecture through flight deployment, working hybrid (3 days on-site in San Jose) with cross-functional aerospace teams.
What you'll do
- Design and implement GPU compute kernels (CUDA) for image processing, signal processing, and ML inference on orbiting satellites
- Architect end-to-end GPU pipelines ingesting live sensor data and optimizing for real-time and power constraints
- Partner with customers to port and optimize their algorithms and models for flight-ready GPU execution
- Profile and optimize GPU workloads for occupancy, memory patterns, and real-time deadline compliance
- Own verification, validation, and CI/CD infrastructure including cross-compilation toolchains and containerized builds
- Collaborate with flight software, FPGA, and hardware teams on interfaces, data formats, and timing budgets
What they're looking for
- CUDA, OpenCL, or HIP GPU programming
- C/C++ and Python with deep systems knowledge
- GPU architecture and kernel optimization
- Embedded Linux development (cross-compilation, device trees)
- Image or RF signal processing
- ML inference optimization and quantization
- CI/CD and containerization for embedded systems
- Technical communication and design documentation
Benefits
- Competitive equity grant
- Comprehensive benefits package
- Hybrid work arrangement (3 days on-site in San Jose, CA)
- Mission-critical aerospace projects
- Cross-functional collaboration with expert teams
- Opportunity to work on next-generation satellite technology
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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 GPU kernel you optimized for production—what metrics did you target and what techniques improved performance?
- Describe your experience porting an algorithm or model to embedded GPU hardware; what were the key constraints and trade-offs?