Design, build, and operate mission-optimized satellite constellations with Muon Space. Backed by Air Street.
About the role
Muon Space is seeking a GPU Engineer to join our High Performance Compute (HPC) team. You will design and develop GPU-accelerated software that runs onboard orbiting satellites, powering mission-critical workloads such as Earth-imaging sensor processing, RF signal analysis, and onboard AI/ML inference.
What they're looking for
- Bachelor's or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Applied Math, or a related technical field, plus 3+ years of professional experience developing GPU-accelerated software
- Strong proficiency in C/C++ or Rust, and Python, with deep familiarity with memory management, concurrency, and performance-oriented programming in production systems
- Demonstrated production experience shipping GPU-accelerated software using one or more of CUDA, OpenCL, HIP, or similar
- Proven experience developing and debugging on embedded Linux (e.g., Ubuntu on Nvidia Jetson/IGX-class platforms), including cross-compilation, device tree basics, and userspace/kernel driver interaction
- Ability to write Linux userspace software integrating GPU compute with the rest of the system via shared memory or similar mechanisms
- Ability to work directly with internal and external customers, understanding their algorithms and models, and guiding them through the process of adapting those workloads to run efficiently on embedded GPU hardware
More about this role
Muon Space is seeking a GPU Engineer to join our High Performance Compute (HPC) team. You will design and develop GPU-accelerated software that runs onboard orbiting satellites, powering mission-critical workloads such as Earth-imaging sensor processing, RF signal analysis, and onboard AI/ML inference.
On our satellites, GPUs will support mission-critical functions including Earth imaging sensor analysis, radio signal analysis, and high-performance onboard compute acceleration. You'll work across the full development lifecycle: feasibility, concept, architecture, design, implementation, verification, lab qualification, and deployment to flight, collaborating closely with FPGA engineers, flight software, payload and system hardware, and mission operations teams.
This position is hybrid and requires working on-site in our San Jose, CA office three days per week.
- Design and implement GPU compute kernels (e.g. CUDA) for onboard image processing, radio signal processing, and ML inference workloads.
- Architect end-to-end GPU pipelines that ingest live sensor data (optical and radio), process it on GPU, and hand results off to downlink or CPU-based decision-making subsystems.
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